DETECTION OF ILLICIT DRUG USE IN BLOOD: A VALIDATION STUDY OF SOLID PHASE EXTRACTION COUPLED WITH LIQUID CHROMATOGRAPHY AND TANDEM MASS SPECTROMETRY
Latisha Pipes
A Thesis
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
SELECT ONE:
May 2020
Committee:
Jon Sprague, Advisor
Phillip Gibbs
Travis Worst
© 2020
Latisha Pipes
All Rights Reserved iii ABSTRACT
Jon Sprague, Advisor
With the increase of driving while under the influence of drugs and the legalization of marijuana there is a need to develop and validate solid phase extraction coupled with liquid chromatography tandem mass spectrometry methods using blood. The protocol that was followed was in the Clinical Lab Consulting Method Validation manual. The parameters that were tested were linearity/reportable range, limit of detection, and precision. The linearity/reportable range showed that 80/81 of the analytes had an average R2 value of 0.950 or higher. The exception was cannabidiol with an average R2 value of 0.932. The limit of detection showed that a cutoff of 1 ng/mL was sufficient for 80/81 analytes. THC requires a cutoff of 2 ng/mL. The precision showed issues with negative mode analytes such as cannabidiol and barbiturates. iv
Dedicated to my parents, Mr. Michael Pipes and Mrs. Mary Pipes who have offered their love and support every step of the way through this research. Also, to my dog, Freckles Pipes for his
constant companionship through the long nights of writing. v ACKNOWLEDGMENTS
I would like to thank the National Highway Traffic Safety Administration for funding this research.
I would like to thank all of my committee members for their knowledge and advice that they have passed down to me and for their patience through all of the setbacks that were encountered.
I would like to thank my advisor, Dr. Jon Sprague, for encouraging me to build confidence in myself and my abilities. Also, for taking the time to teach me throughout this research project based on where I started to where I am today. For always taking the time to come in on weekends when issues would arise. Lastly, for going above and beyond for his students.
I would also like to thank Dr. Phillip Gibbs, for mentoring me along every step of this research process. The knowledge that I have gained from him will help me to begin a career and for that I am grateful.
I would like to thank Dr. Travis Worst, for calming me down multiple times when I felt overwhelmed and stressed. Also, for being a great professor and academic advisor.
I would like to thank a fellow Graduate student, Amal Aburahma, for taking time out of her busy schedule to help me prepare my samples. Her help was greatly appreciated.
I would also like to thank my fellow research partner, Nathan Bunch, for his support.
Lastly, I would like to thank my parents for their constant encouragement, love, and support. Also, for calming me down late at night over the phone when the stress levels were high. vi
TABLE OF CONTENTS
Page
CHAPTER 1. INTRODUCTION & BACKGROUND ...... 1
1.1 Drug Abuse and the Legal System...... 1
1.2 Pharmacology, Toxicology, Pharmacokinetics and Tolerance of a Drug ...... 2
1.3 Blood ...... 7
1.4 Solid Phase Extraction ...... 10
1.5 Precipitating Agent ...... 12
1.6 Liquid Chromatography ...... 15
1.7 Mass Spectrometry...... 20
1.8 Example of an Optimization ...... 23
1.9 Internal Standards ...... 25
CHAPTER 2. RESEARCH FOCUS ...... 31
CHAPTER 3. MATERIALS AND EXPERIMENTAL DESIGN ...... 32
3.1 Materials ...... 32
3.2 Experimental Design ...... 33
CHAPTER 4. RESULTS ...... 41
4.1 Linearity/Reportable Range ...... 41
4.2 Limit of Detection ...... 45
4.3 Precision ...... 51
CHAPTER 5. DISCUSSION ...... 65
5.1 Linearity/Reportable Range ...... 65
5.2 Limit of Detection ...... 66 vii
5.3 Precision ...... 69
5.4 Overall Analysis...... 70
CHAPTER 6. CONCLUSION...... 73
REFERENCES ...... 75
APPENDIX A. STANDARD ANALYTES ...... 80
APPENDIX B. INTERNAL STANDARDS ...... 81
APPENDIX C. CALIBRATION CURVE STOCK SOLUTION PREP GUIDE ...... 82
APPENDIX D. HIGH QC STOCK SOLUTION PREP GUIDE ...... 84
APPENDIX E. LOW QC STOCK SOLUTION PREP GUIDE...... 86
APPENDIX F. INTERNAL STANDARD STOCK SOLUTION PREP GUIDE ...... 88
APPENDIX G. CALIBRATION CURVE STOCK AND QC STOCK PREP GUIDE ...... 89
APPENDIX H. ARTIFICIAL SERUM CALIBRATORS AND QC PREP GUIDE ...... 90
APPENDIX I. BATCH FILE EXAMPLE...... 91
APPENDIX J. STANDARD ANALYTES WITH THEIR INTERNAL STANDARD
ASSOCIATIONS ...... 92
APPENDIX K. STANDARD ANALYTE METHOD PARAMETERS ...... 93
APPENDIX L. INTERNAL STANDARD METHOD PARAMETERS ...... 94 viii
LIST OF TABLES
Table Page
1 Linearity/Reportable Range Results ...... 42
2 Limit of Detection Results ...... 47
3 HiQC Precision Results ...... 53
4 HiQC Spread of the Precision Results with Caffeine ...... 55
5 HiQC Spread of the Precision Results without Caffeine ...... 55
6 LoQC Precision Results ...... 57
7 LoQC Spread of the Precision Results with Caffeine ...... 59
8 LoQC Spread of the Precision Results without Caffeine...... 59
9 SST Precision Results ...... 61
10 SST Spread of the Precision Results with Caffeine ...... 63
11 SST Spread of the Precision Results without Caffeine ...... 63 ix
LIST OF ABBREVIATIONS
Abbreviation Meaning ACN Acetonitrile ADME Absorption Distribution Metabolism Excretion ANOVA Analysis of Variance AS Artificial Serum Avg R2 Average R2 BCI Bureau of Criminal Investigation CB Cannabinoid CCS Calibration Curve Stock CCSS Calibration Curve Stock Solution CE Collision Energy CO Cutoff CV% Coefficient of Variance Percentage Da Daltons DL Desolvation Line DOF Degrees of Freedom ESI Electrospray Ionization GC/MS Gas Chromatography Mass Spectrometry HiQC High Quality Control HiQC_SS High Quality Control Stock Solution HPLC High Performance Liquid Chromatography IPA Isopropyl Alcohol ISTD_SS Internal Standard Stock Solution LC Liquid Chromatography LC-MS/MS Liquid Chromatography Tandem Mass Spectrometry LLOQ Lower Limit of Quantitation LOD Limit of Detection LOQ Limit of Quantitation LoQC Low Quality Control LoQC_SS Low Quality Control Stock Solution MRM Multiple Reaction Monitoring MS/MS Tandem Mass Spectrometry NAC Nucleus Accumbens NegQC Negative Quality Control NHTSA National Highway Traffic Safety Administration OF Oral Fluid OSHP Ohio State Highway Patrol PEEK Polyether Ether Ketone PFC Prefrontal Cortex PSI Pounds of Pressure per Square Inch x
Q1 Quadrupole One q2 quadrupole Two Q3 Quadrupole Three QCS Quality Control Stocks QCs Quality Controls RSS Residual Sum of Squares SD Standard Deviation SDP Standard Deviation of a Population SLE Supported Liquid Extraction SPE Solid Phase Extraction SST System Suitability Test THC Tetrahydrocannabinol THC-OH 11-Hydroxy-Tetrahydrocannabinol UHP Ultra-High Purity 1
CHAPTER 1. INTRODUCTION & BACKGROUND
1.1 Drug Abuse and the Legal System
Driving while under the influence of illicit drugs has become a significant problem nationally. According to the National Highway Traffic Safety Administration (NHTSA), in a
2013-2014 survey, approximately 20 percent of drivers were under the influence of illicit drugs and/or over-the-counter medications while operating a motor vehicle (Kitch, 2019). Also, according to the NHTSA, the number of deceased drivers that were under the influence of marijuana at the time of the crash doubled from 2007 to 2015 (Kitch, 2019). The detection of these illicit drugs in blood is gaining importance as more lives are being lost to drug impaired driving. With the amount of drugs leading to impaired drivers on the road, there is a need for fast, reproducible, and sensitive detection methods for blood.
Currently, the Ohio Bureau of Criminal Investigation (BCI) does not test for illicit drug use in biological samples. BCI chemists are mainly concerned with determining if illicit drugs are physically present and in what amount. With the legalization of recreational marijuana on the rise, at the state level, it is important to have validated methods in place to be able to quantify the drugs in biological samples. As of March 2020, at least twelve states have already passed legislation that has made recreational marijuana use legal (Skelton, 2020). Once recreational marijuana use becomes legalized in other states, the zero tolerance laws will have to be replaced with new laws. Along with new laws, will come new methods for detecting and quantifying these types of drugs. These new methods will need to be validated in order to confirm that they can produce precise and accurate results. The results obtained with validated methods will then aid in court proceedings along with the new laws. 2
Much debate surrounds the use of per se limit laws and whether it will work for marijuana. There are many issues associated with using per se limit laws when it comes to marijuana. Researchers and law makers would first need to determine a set cutoff level of marijuana a person could have in their system that is likely to cause impairment issues while driving (Wong, 2014). If a person was found to be operating a motor vehicle at or above that predetermined per se limit for marijuana, then that person could be arrested and prosecuted
(Wong, 2014). While this sounds like a good idea in theory, it is not that simple for marijuana or other drugs for that matter.
1.2 Pharmacology, Toxicology, Pharmacokinetics and Tolerance of a Drug
In order to establish laws and determine drug concentration cutoff limits, the pharmacodynamic, pharmacokinetic and toxicologic effects of the drug need to be thoroughly understood. In this section, marijuana will be used as the prototypical drug in order to describe the influence pharmacology, toxicology, pharmacokinetics, and tolerance can have on establishing drug laws. Marijuana is composed of hundreds of chemical compounds. However, a single compound, tetrahydrocannabinol (THC), has been determined to be the primary constituent responsible for the psychoactive effects of marijuana (Ashton, 2001; Compton,
2017). A psychoactive drug is often used because it affects the central nervous system causing a change in mood, perception, coordination, thought process, and even behavior (Hanson, 2018, p.451). The problem then becomes how much of a drug is too much? This is where it gets difficult to set the legal drug concentration limit. Not every individual metabolizes or responds to a drug in the same way. A predetermined set drug concentration for one person can cause impairing effects but that same concentration for another person could show no impairing effects
(Wong, 2014). 3
Pharmacology is described as the effects that a drug has on a person’s body. The pharmacology of THC includes binding to the cannabinoid (CB) receptors. The body has an endogenous cannabinoid system (aka. endocannabinoid system) that utilizes anandamide that binds to the CB receptors (Ashton, 2001). This binding to the CB1 receptor leads to an increase in the amount of dopamine that is released in the nucleus accumbens (NAC) (Ashton, 2001). This increase in dopamine in the NAC is responsible for the euphoria that individuals experience while under the influence of THC and other drugs of abuse. This euphoric feeling is felt almost instantaneously after inhalation of marijuana (Hanson, 2018, p.436). THC also has effects on the prefrontal cortex (PFC) in the brain. This region of the brain is responsible for short-term memory and decision making. This is caused by the decrease in glutamate, which is a stimulatory neurotransmitter, found in the PFC (Fan, 2010). Long-term marijuana use can also have effects on the hippocampus, which is responsible for some short-term memory as well as long-term memory (Fan, 2010).
A person can also experience toxicological effects of a drug. Toxicology refers to the adverse effects that a drug can have on a person’s body. Along with the euphoria, a person can also experience acute toxicological effects such as dysphoria (depending on the user), loss of coordination and balance, decreased reaction times, altered perception of time and memory loss
(Hanson, 2018, p.436 and 449). These effects can last up to three hours after inhalation (Hanson,
2018, p.436). This is where the issue arises when individuals have used THC a few hours before getting behind the wheel of a car; they think they are fine to drive but may still be experiencing the previously stated effects. Chronic- or long-term use of marijuana can cause short-term memory loss, learning difficulties, respiratory problems, mental illness, fertility issues, and marijuana has addictive properties (Hanson, 2018, p.449). The reality is that marijuana impairs 4 an individuals’ ability to operate a vehicle safely, but this information will not prevent them from getting behind the wheel of a 2,000-pound vehicle and potentially resulting in accidents and fatalities. According to Wong et al., (2014) marijuana is the most prevalent drug, other than alcohol, to be found in individuals operating a vehicle. Therefore, forensic laboratories will need to be able to not only identify the illicit drugs in a biological sample but will also need to be able to quantify the amount of the illicit drugs in biological samples such as blood. The amount of the illicit drugs in the persons system at the time of being pulled over or at the time of the accident could determine what charges they will be charged with.
The pharmacokinetics of a drug is the movement of a drug through a person’s body based on the route of administration to the point of excretion and can include how a drug is absorbed, distributed, metabolized, and excreted (ADME). The difficulty associated with the detection of
THC and establishing a per se limit arises as a result of its pharmacokinetic properties. With the inhalation of marijuana, the effects of THC are felt instantly. The reason for this is because the
THC is absorbed in the lungs causing it to reach the bloodstream almost instantly (Ashton,
2001). Once in the bloodstream it does not take long for THC to enter the brain. If THC is taken orally as with edibles, the effects can last longer because absorption takes place over time in the small intestine (Ashton, 2001). While the effects may last longer, the amount of time it takes to feel those effects is going to take longer than if the THC is inhaled. Due to THC being ingested, rather than inhaled, the blood concentrations are going to be significantly lower because of metabolism in the liver (Ashton, 2001). THC is distributed throughout the body in the blood and is extremely fat soluble, so it likes to stick to fat. THC concentrations reach a maximum in a person’s system in about 4-5 days after one use (Ashton, 2001). The half-life is approximately 7 days, which means that it takes about 30 days or 1 month to fully eliminate THC from the body 5 completely after one use (Ashton, 2001). Because of the lipophilic nature of THC, it is stored in adipose tissue and can be released slowly overtime. The above information was based on a single use of marijuana that has been administered through inhalation. If a person is a chronic user, then the THC concentration in their body is going to continue to increase faster than they can eliminate it. This means that the person may still be feeling the effects of the THC over the course of time it takes to completely excrete the THC from their body.
Some of the metabolites of THC can also be psychoactive. An example of this would be the metabolite 11-hydroxy THC (THC-OH). Not only does the parent compound of THC have a long half-life but so do the metabolites of THC (Ashton, 2001). Lastly, the majority (65%) of the metabolites are excreted in the gut, meaning that they are reabsorbed and roughly (25%) are excreted in the urine (Ashton, 2001). Given this information, it makes it difficult to compare
THC concentration in blood (serum) and urine (Ashton, 2001). Other difficulties associated with
THC include tolerance and behavioral compensation.
Depending on the individual and how often the person administers the THC, they can build their tolerance to the drug. Tolerance is defined as the relationship between the repeated use of a drug at a constant dose, that causes impairing effects to decrease (Hanson, 2018, p.171).
Essentially, as a person uses the same amount of THC over and over their level of “high” that produces the impairing effects decreases. This is more of a problem for occasional users versus chronic users as far as impairment because they have not built that tolerance and will be more susceptible to the impairing effects (Wong, 2014).
As a drug is used over and over the body will adapt to a new normal. The body has a narrow range of optimal conditions in which it likes to maintain to function normally, known as homeostasis (Hanson, 2018, p.136). When a person uses a drug and depending on where the drug 6 binds and what effects it produces, can change their neurotransmitters which in effect changes their homeostasis (Hanson, 2018, p.144). The body recognizes these changes and tries to compensate for them (Hanson, 2018, p.171). For an occasional user, the body will make the adjustments only as the person is under the influence of the drug. For a regular or chronic drug user, the body will recognize that this new drug induced state is their new normal (Hanson, 2018, p.172). Essentially the person has built up a tolerance to the drugs and they will no longer have the same impairing effects that they once had when they were a novice or an occasional user.
This is also why when drug addicts are in rehab and have been sober for months and they relapse they will often overdose. Once the drug is taken away for an extended amount of time the body will slowly set a new normal for the person (Hanson, 2018, p.172). This new normal will never be the same normal the person experienced before they ever started using drugs. What happens is the person relapses and thinks that their body can handle the same amount of the drug that they had previously taken before rehab and their body tries to compensate for it but it actually overcompensates because the persons tolerance has decreased while in rehab and the person experiences an overdose (Hanson, 2018, p.160). The person’s tolerance is partially responsible for their level of impairment depending on whether they are an occasional or regular drug user
(Wong, 2014).
There is also a term used for the person’s ability to function normally while under the influence of drugs called behavioral compensation (Hanson, 2018, p.174; Wong, 2014). This is often mistaken for tolerance. The majority of these individuals are chronic drug users and have developed a way to function normally despite having an amount of a drug in their system that should be causing impairing effects (Hanson, 2018, p.174). The problem then becomes if the 7 individuals are driving while under the influence of THC, but they are not showing signs of impairment due to tolerance or behavioral compensation, are they really impaired?
1.3 Blood
Whole blood consists of cells, enzymes, and proteins. The fluid part of blood is referred to as the plasma. The cellular part consists of erythrocytes, which are denucleated red blood cells, leukocytes, which are the white blood cells, and thrombocytes, which are platelets that are responsible for the blood clotting (Gunn, 2019, p.83). There is also a difference between plasma and serum. Plasma is the top liquid layer of blood without the cells, when centrifuged, whereas serum includes the plasma but excludes the clotting factors (Compton, 2017). The main difference between serum and plasma is that serum does not include fibrinogen whereas plasma does (Zhao, 2018). Fibrinogen is a glycoprotein that can be chemically altered into fibrin to promote blood clotting when an injury arises. However, there are still proteins and phospholipids that are still present within the serum. This can cause problems when analyzing serum for drug detection and quantitation that will be discussed later. When blood is drawn, it is usually centrifuged into its counterparts. Typically, the serum layer is used for testing. The serum will need to be filtered first in order to “clean up” the sample. This procedure will also be discussed later in the methods section.
Currently, detecting recent drug use in blood has been considered the “gold-standard”
(Compton, 2017; Stockmann, 2013). According to Stockmann (2013), blood is the gold standard because this matrix is the least susceptible to be affected by collection errors. Another reason is because the parent (main component) drug can be detected within the blood indicating recent use
(Knittel, 2016). Other matrices, such as urine, will detect mostly the metabolites of drugs, which depending on the drug and its half-life, can cause issues in establishing when the drug was 8
administered (Knittel, 2016). When a driver has been suspected of driving while under the
influence of a drug, the officers will require the driver to have their blood drawn (Stockmann,
2013). This is because the officer wants to know what the driver may be under the influence of at
the time in which they were pulled over or when an accident occurred.
Now, this can become a problem when the officer must obtain a signed warrant by a judge because drawing blood is considered invasive under the Fourth Amendment (Stockmann,
2013). The process of obtaining a warrant, transporting the driver to a hospital, contacting a phlebotomist, and drawing the person’s blood all take time (Stockmann, 2013). According to
Stockmann (2013), on average it can take anywhere from one to four hours to get a signed search warrant. This can create a problem due to the half-life of drugs in the blood. Some drugs can have an extremely short half-life that could be partially or fully eliminated from the persons system based on the pharmacokinetics of the drug. The drug concentration can rapidly decrease in the amount of time it takes to get the driver from the scene to the hospital, which could result in the loss of evidence (Stockmann, 2013). Due to the time delay in obtaining a blood sample, the drug concentration may not be an actual representation of the concentration of a substance that was in the persons system at the time of being pulled over or the accident (Compton, 2017).
Other factors can also influence how quickly a drug is excreted from the blood/body.
These factors can include the person’s genetic makeup of whether they are poor, intermediate, or good metabolizers, body weight, sex (male or female), method of use, and sometimes if they have eaten (Stockmann, 2013). How fast a drug is excreted from the blood or body can also depend on the other drugs and or alcohol that the person may have taken or ingested and in what amounts (Stockmann, 2013). The above factors can all affect the drug concentration in a person’s 9 blood sample. Usually the drug concentration in blood will be at its peak right after or soon after exposure, depending on the route of administration.
From a forensic prospective, there is a need to improve the implementation and use of blood or to identify an alternative biological matrix as the gold standard for determining if a subject is operating a vehicle under the influence of a drug. For example, the collection of oral fluid (OF) roadside is making its way from the experimental stage to roadside use. Currently, OF is considered only a presumptive test. Therefore, the results from a roadside OF test will need to be confirmed using whole blood. Thus, resulting in the need to validate efficient, sensitive, reliable, and reproducible methods for detecting drugs in whole blood.
The Ohio State Highway Patrol (OSHP) currently has several protocols to test blood for illicit drugs. OSHP has a procedure set in place to screen for drugs in blood using the Liquid
Chromatography coupled with tandem Mass Spectrometry (LC-MS/MS) instrument (OSHP,
2019). The blood screen is a qualitative analysis to test for the presence of a drug (OSHP, 2019).
The blood screen uses a “crash solution” of cold acetonitrile, with multiple vortex and centrifugation steps, evaporation to dryness with nitrogen, and reconstitution of the drugs with methanol (OSHP, 2019). OSHP quantifies most of the drugs in blood using a LC-MS/MS instrument as described in the OSHP Crime Laboratory Toxicology Procedure Manual for panel
1 and panel 2 (OSHP, 2017; OSHP, 2018). The sample preparation method for quantitating drugs includes the use of supported liquid extraction (SLE) with a vacuum manifold and an SPE
Dry instrument that dries down the sample under nitrogen gas (OSHP, 2017; OSHP, 2018). SLE uses a cartridge that will contain the sample and in order to elute the sample a solvent must be used that will cause the analytes to elute with the solvent, while the proteins are left behind on the cartridge. However, there is an exception, which is the quantitation of cannabinoids in blood 10
(OSHP, 2015). Cannabinoids are analyzed on a Gas Chromatograph with a Mass Spectrometer
(GC/MS) instrument (OSHP, 2015). Samples that contain cannabinoids are derivatized and then injected into the GC/MS (OSHP, 2015). The sample preparation methods for the cannabinoids includes extraction with traditional solid phase extraction (SPE) in which the cartridges or columns are first conditioned, then the samples are drawn through the column, the columns are then rinsed and dried, and lastly acetone is drawn through the columns (OSHP, 2015). Again, the samples are evaporated using nitrogen gas (OSHP, 2015). Their protocols for precipitating out the phospholipids can be time consuming due to multiple steps. They utilize several reagents which can be costly. There is, therefore, a critical need for new methods to be validated to increase the efficiency, decrease the amount of time spent per sample, and to decrease the overall cost of testing.
1.4 Solid Phase Extraction
The filtration method that was used for this research was (SPE). The solid phase extraction was done using Supelco’s HybridSPE-Phospholipid Technology (Sigma-Aldrich, St.
Louis, MO). The SPE process removes phospholipids and proteins from the serum that can cause interference with the detection of the illicit drugs in the samples. Examples of these interferants is that they can co-elute, and they can cause ion suppression (Craig, 2008). The co-elution causes the ion suppression affecting the analytes mass spectrometer signal (Craig, 2008). This can impact the accuracy of the results and can also affect reproducibility. Essentially what is occurring is that the phospholipids can co-elute with the analytes, meaning at the same time, which in effect causes the ion-suppression or a false decrease of the signal for that analyte
(Craig, 2008). The phospholipids can also, stick to the column and then elute randomly later.
This random elution of the phospholipids will affect the reproducibility, so it is imperative to 11 remove the phospholipids before any analysis takes place. The co-elution and ion-suppression are the main concerns with the liquid-liquid extraction that was mentioned in the OSHP methods.
Phospholipids and proteins can also cause mechanical problems for the LC/MS/MS process.
These larger molecules can clog the polyether ether ketone (PEEK) tubing within the LC, and they can also decrease the life of the guard column and potentially clog the biphenyl column causing an increase in backpressure and downtime (Sedgwick, 1991). This was observed in this research and extra cleaning measures were taken to keep the instrument clean, which will be discussed later.
The traditional SPE methods can be time consuming and can include complex methods.
These traditional SPE methods utilize a hydrophobic retention mechanism in order to extract the phospholipids from the sample (Sigma-Aldrich, 2013). If the analytes of interest are also hydrophobic then the traditional SPE methods can cause a percent recovery issue for those analytes. The hydrophobic analytes will be retained and removed from the samples along with the phospholipids (Sigma-Aldrich, 2013). This will negatively impact the results causing a decrease in analyte recovery and poor results (Sigma-Aldrich, 2013). As stated previously, this could also be an issue if a person driving was under the influence of drugs at the time of being pulled over or at the time of an accident and was found to be under the legal limit due to loss of the analyte in the SPE procedure. Better SPE methods are needed to prevent erroneous results.
According to Sigma-Aldrich, (2013) they have a Supelco HybridSPE-Phospholipid
Technology system that can “effectively remove phospholipids and proteins for accurate and reproducible results”. The advantages of using this SPE system is that it is “simple, fast and easy to use by offering standardized methods with minimal method development” (Sigma-Aldrich,
2013). The other advantage to using the HybridSPE-Phospholipid Technology system is that it is 12 capable of removing both proteins and phospholipids at the same time without retaining any other hydrophobic compounds or analytes of interest (Sigma-Aldrich, 2013). Due to these advantages, this system was used for this validation study.
The chemistry behind the Supelco HybridSPE-Phospholipid Technology is different from the traditional SPE hydrophobic retention mechanism. This new technique couples the methodology from traditional precipitating agents, used for the separation of proteins, and the specificity of SPE (Craig, 2008). While the traditional methods utilize a hydrophobic retention mechanism, the HybridSPE utilizes a different retention mechanism (Sigma-Aldrich, 2013). This unique mechanism has the capability to extract phospholipids from very hydrophobic analytes
(Sigma-Aldrich, 2013). The SPE works on the premise that the phospholipids will interact with the zirconia coated silica atoms based on their chemistry. When the phospholipids donate electrons and the zirconia accepts the electrons, it results in the retention of the phospholipids in the hydrophobic filter (Craig, 2008). This filter acts as a packed bed which allows for quick flow rates and the removal of phospholipids (Craig, 2008). This chemistry between the hydrophobic filter and the phospholipids and the combined protein removal with a precipitating agent makes this technique ideal for sample clean-up.
1.5 Precipitating Agent
A precipitating agent can also be referred to as a “crash solution.” A precipitating agent can be used in conjunction with the SPE in order to crash out or precipitate out proteins from the whole blood. The precipitating agent that was used in this research was a 1% solution of formic acid in acetonitrile. Acetonitrile is an organic, polar, aprotic solution (PubChem, Acetonitrile).
Aprotic means that its chemical structure contains no oxygen-hydrogen bonds or nitrogen- hydrogen bonds. Formic acid is an organic, polar, protic solution (PubChem, Formic Acid). The 13 formic acid is protic because it contains an oxygen-hydrogen bond. The formic acid will cleave the proteins into their constituent peptides (PubChem, Formic Acid). This cleavage will either take place at the carbon or the nitrogen terminal (PubChem, Formic Acid). The residue must be an aspartate for this cleavage to occur according to PubChem. This crash solution when mixed with a serum sample will cause the proteins to “crash out” of solution and create a precipitate at the bottom of the glass vial. Ideally, the supernatant would be drawn off and aliquoted into the
HybridSPE Phospholipid cartridges where any other remaining proteins and phospholipids would be extracted from the blood.
The mechanism behind the precipitating agent is that the organic solvents such as acetonitrile and formic acid will cause the proteins to crash out of solution by removing the water that is surrounding the proteins (Sedgwick, 1991). This will cause the proteins to stick together due to ionic attraction and ultimately forming protein aggregates (Sedgwick, 1991). The removing of water is based on the fact that acetonitrile is a polar molecule and water is also a polar molecule so like dissolves like. With the removal of the water from the proteins it allows the proteins to have more charged functional groups (Sedgwick, 1991). These charges can pull amino acids into the aggregate resulting in cleaning up the serum sample further (Sedgwick,
1991). In order to decrease the solubility of the proteins and to get them to precipitate out of solution, the surrounding water needs to be removed (Zhao, 2018). According to Zhao et al.,
(2018) when acetonitrile is used as the precipitating agent, the resulting precipitating proteins are large and yellow. A visual observation of the supernatant can indicate if the precipitating agent was efficient at doing its job. If the supernatant is cloudy that can be an indication that the proteins did not precipitate out of solution very well (Zhao, 2018). On the other hand, if the supernatant is mostly clear then that can indicate that the precipitating agent did its job and most 14 of the proteins were precipitated out of solution (Zhao, 2018). In order to achieve a mostly clear supernatant it has been shown that a ratio of 3:1 or 2:1 of acetonitrile to blood will give the best results for precipitating out proteins (Zhao, 2018). A problem that may be encountered when using a higher ratio of crash solution to blood such as 3:1 respectively, is dilution of the analytes of interest.
The 1% formic acid is added to the precipitating agent to decrease the amount of protein binding (Zhao, 2018). The reduced protein binding is referring to the proteins binding to the frit or membrane during the filtration process as described above. When proteins bind to the frit or membrane of the HybridSPE Phospholipid cartridge, they can clog the cartridge causing it to be more difficult for the rest of the solution to pass through. Zhao et al., (2018) also cautions the use of formic acid or any acidic solution with whole blood. The reason being that the acidic conditions of the solution can also extract the red or brown color of the blood into the supernatant from the hemoglobin in the whole blood (Zhao, 2018). Also, the amount of proteins can vary from whole blood to serum. While whole blood has the greatest amount of proteins, plasma has an intermediate amount of proteins, and serum has the least amount of proteins for a given volume, according to Zhao et al (2018). More specifically plasma can be composed of approximately 6-8% of proteins (Sedgwick, 1991). In order to get the proteins to precipitate out of solution further, there has been research done on the temperature of the crash solution.
While room temperature crash solution will give an effective result, cold crash solution can improve the separation. The cold environment of the crash solution can help to denature or promote the unfolding of the proteins (Privalov, 1990). Once this unfolding occurs the proteins nonpolar surfaces are exposed to the polar solution. Another contributing factor is the Van der
Waals interactions. More specifically the nonpolar surfaces of the proteins and the Van der 15
Waals interactions (Privalov, 1990). This causes the proteins to interact with other neighboring proteins causing them to precipitate out of solution. Once the proteins and phospholipids have been precipitated and filtered out of solution the blood sample should be clean enough to be run on the Liquid Chromatograph (LC).
1.6 Liquid Chromatography
The overall goal of chromatography is to be able to separate, identify, quantify, and differentiate between analytes in different toxicological matrices. How well the analytes are separated, identified, quantified, and differentiated depends on several factors and parameters of the liquid chromatograph instrument. These factors can include but are not limited to the following: mobile phases, stationary phase and method type and length. Another main factor is the polarity of the mobile phases, stationary phase, and of the analytes. As stated by Skoog et al.,
“the polarities of the various analyte functional groups increase in the following order: hydrocarbons < ethers < esters < ketones < aldehydes < amides < amines < alcohols (Skoog,
2018, p.759). Other factors can be attributed to the toxicological matrix, sample preparation, etc., but those will be discussed later.
Liquid chromatography utilizes liquid mobile phases to separate analytes based on retention time. If only one mobile phase is used then it is an isocratic elution (Skoog, 2018, p.749). If more than one mobile phase is used with varying polarity and composition during the run then it is a gradient elution (Skoog, 2018, p.749). In order to change the composition of the mobile phases during a separation run, a method will need to be developed. This method will have the capability of altering the composition of each of the mobile phases throughout the data acquisition run time. The use of gradient elution methods is to save time for a higher sample throughput and to obtain a better sample separation. In order to run a gradient method with two 16 mobile phases a reciprocating pump system will need to be utilized. These reciprocating pumps are responsible for controlling the flow of each mobile phase based on the parameters set up in the method.
The stationary phase consists of a packed column in liquid chromatography. The normal columns that are typically used for liquid chromatography are either a C18 or C8 (Skoog, 2018, p.758). The chemistry behind the C18 and the C8 columns is that they have a straight chain alkyl chemistry. These columns are typically used for reverse phase liquid chromatography (Skoog,
2018, p.758). Reversed phase chromatography is where the stationary phase, such as the C18 column, is nonpolar and the mobile phase is polar (Skoog, 2018, p.758). This would cause the early eluting compounds to be more polar because they do not want to interact with the nonpolar stationary phase for very long. Normal phase chromatography utilizes a polar stationary phase and nonpolar mobile phases (Skoog, 2018, p.758). This means that the early eluting compounds would be more nonpolar because they do not want to interact with the polar stationary phase.
The determining factor of whether a column is polar or nonpolar depends on its internal packing chemistry. The C18 columns are usually made out of octadecylsilane which is good for reversed phase liquid chromatography (Skoog, 2018, p. 758). According to Restek (2018) and Lupo
(2016), the C18 columns show a strong hydrophobic retention. The main issue with using C18 columns is that they are not selective when it comes to aromatic compounds (Restek, 2018). In order to combat these issues, Restek developed a biphenyl column.
The biphenyl column is also used with reversed phase liquid chromatography and utilizes a chemistry known as pi-pi stacking (Lupo, 2016). With pi-pi stacking, the biphenyl column has a better selectivity over the C18 columns. The biphenyl column also does not use an alkyl chain linker like the C18. Instead the biphenyl uses an aryl linker to bind the two phenyl rings in the pi- 17
pi stacking order (Restek, 2018). This linker that is bonded to the silica surface makes the
biphenyl column more hydrophobic and can also increase the size of the electron cloud with two
phenyl rings as opposed to one phenyl ring (Lupo, 2016; Restek, 2018). Based on the above
information regarding the biphenyl column, it has an improved aromatic selectivity as opposed to
the C18 column and has an improved hydrophobic retention as well (Lupo, 2016; Restek, 2018).
An example of this would be the successful separation of codeine and hydrocodone using the
Raptor biphenyl column (Waybright, 2016). The two analytes had almost the same multiple
reaction monitoring (MRM) transitions, so the fact that they could be separated and eluted at
different times was related to the aromatic selectivity of the biphenyl column (Waybright, 2016).
These are advantages of using the biphenyl column as opposed to the C18 column for drug
analysis.
Drugs often contain multiple rings, such as benzodiazepines, they can be highly
conjugated to increase resonance and stability of the compound, and they normally have different
functional groups off the ring structure. All of these factors are important when determining what analytical column will work best for the type of separation that will be performed. The biphenyl column has an increased aromatic selectivity that will be good for separating and differentiating between drug compounds. Also, according to Restek (2018), the biphenyl column not only provides good retention for hydrophobic compounds but also hydrophilic compounds. Given this selectivity, a biphenyl column is capable of distinguishing or separating co-eluting compounds.
The biphenyl column also works well with gradient elution methods when methanol is one of the mobile phases. According to Lupo (2016) and Restek (2018), the biphenyl column can have an increased selectivity and can be optimized for particular separations with the use of gradient methods. The way in which the biphenyl column operates depends on the mobile phase used. 18
The organic solvent has the most impact on the biphenyl column. Yang et al., (2005) compared the use of acetonitrile and methanol. The organic mobile phase can affect the way in which the separation takes place by either imploring hydrophobic or pi-pi interactions (Yang,
2005). Methanol is a weaker organic solvent than acetonitrile because it contains one less carbon.
If acetonitrile were to be used as the organic mobile phase it would cause the biphenyl column to act more like a C18 column when it comes to selectivity (Yang, 2005; Restek, 2018). However, if methanol is used as the organic mobile phase then the biphenyl column will utilize the aromatic selectivity of the pi-pi interactions (Yang, 2005; Restek, 2018). This shows that the desired effects of the column can be altered based on the composition of the mobile phases that are chosen to carry out a separation.
For this research, a specific biphenyl column was used called a Raptor. According to
Lupo (2016) and Restek Pure Chromatography (2018), these columns contain superficial porous particles that act as the packing. These specific columns are ideal when the detection source is a mass spectrometer. The reasoning behind this is that the Raptor can reduce ion suppression by retaining the compounds with the earliest retention times (Restek Pure Chromatography, 2018).
One of the many advantages of using a Raptor biphenyl column is that is can promote faster separations because the packing is less porous than normal columns. Along with the faster separation, a biphenyl column is capable of sustaining higher backpressures that result from the speed of the separation (Restek, 2018). According to Restek Pure Chromatography (2018), the
Raptor columns also have a longevity of 1,000 to 3,000 injections when exposed to high backpressures. The effective separation of analytes not only depends on the mobile phases and stationary phases, but also on the method itself. 19
The mobile phases and the column are only as good as they were manufactured to be. A large portion of how well they work together at separating, identifying, quantifying, and differentiating between analytes in different toxicological matrices depends on the method that was developed. Therefore, when new methods are developed, they need to be validated to show that they can produce results that are efficient, reproducible, accurate, and precise.
The method can be thought of as the guide of the separation analysis. The method is responsible for telling the pumps when and how much of their respective mobile phases to pump during the entire data acquisition time known as the flow rate. The method will also tell the oven what temperature to maintain during the whole run. If co-elution takes place, the method may be
altered to get a definitive separation. This can be done by changing the percent composition of
the mobile phases. Also, the dwell time can affect the peak shapes. Dwell time is the amount of
time for the data acquisition to collect points across a peak for a particular analyte based on the
mass to charge ion signal from the mass spectrometer. These points are responsible for the peak
shape and the area under the peak which is related to the concentration of an analyte(s) in a
sample. While a good separation with good, symmetrical, Gaussian peak shapes are ideal the
sample throughput is also a factor when developing an effective and efficient method. In some
cases when a longer method is used and depending on the analytes to be separated, peak tailing
can occur. Where excessive peak tailing is observed the dwell time can be reduced for that
analyte. Depending on where the method is being implemented a high sample throughput may be
needed to meet demands. Quality and quantity should complement each other not overshadow
one another. Once the analytes are separated in the liquid chromatograph instrument, they will
proceed to a mass spectrometer detector. 20
1.7 Mass Spectrometry
Mass spectrometry is used to separate, or filter analytes based on their mass to charge ratio. There are multiple types of mass spectrometers, but the one that was used to conduct this research was a triple quadrupole. The mass spectrometer is made up of an electrospray ionization source (ESI). As the analytes elute from the LC column, they are transported to the ESI source through Peek tubing. At this point they are in liquid form which is not ideal for mass spectrometry. There is a capillary needle located in the ESI source. This capillary is responsible for allowing the flow of the samples to enter the ESI at a rate of a couple of microliters per minute (Skoog, 2018, p.512). Once the samples pass through the capillary, they become a charged spray. This capillary also maintains a charge in kilovolts. The samples are still in liquid
form at this point so they must enter another capillary or desolvation line where the remaining
liquid is evaporated off. This is also where a charge is attached to the samples. This process is
repeated until all the liquid has been evaporated off and the sample contains multiple charges
(Skoog, 2018, p.512). Once the sample has reached its Rayleigh limit, which is the point where
the sample has hit a maximum charge density, the molecules’ surface will no longer be able to
carry the charge (Skoog, 2018, p.512). A mini Coulombic explosion occurs and causes the
already small sample to break apart into many smaller particles (Skoog, 2018, p.512). In order to
evaporate off the liquid portion of the sample, the ESI utilizes a nitrogen heating, drying, and
nebulizing gas. Once the sample is completely in the gas phase and charged it will enter the first
set of quadrupoles.
Quadrupoles are a set of four, parallel, cylindrical rods that are essentially electrodes.
They are positioned in a way that two opposite rods are positive and the other two rods are
negative. In addition to the positive and negative charges from the direct current, a radio 21 frequency alternating current voltage is also applied (Skoog, 2018, p.259). The alternating current voltage is out of phase by 180 degrees (Skoog, 2018, p.259). In order to get the sample analytes from the ESI into the path between the first set of quadrupoles, a potential difference of about 5 to 10 volts is used (Skoog, 2018, p.259). Once the sample analytes are in Q1 the direct and alternating current voltages are constantly increasing as the sample moves through Q1
(Skoog, 2018, p.259). As the quadrupoles change their voltages some of the ions will run into the rods causing them to lose their charges. The ions with a certain mass to charge ratio will not run into the quadrupoles but will reach the next part of the mass spectrometer which could be another set of quadrupoles or the transducer.
The movement of the ions in between the positive and negative quadrupoles is based on the type of voltage and when that voltage is applied. For example, when ions are between the quadrupoles and when no direct current is applied, they will congregate to the middle of the space when a positive charge is applied to the quadrupoles (Skoog, 2018, p.259). The opposite is true when the quadrupoles have a negative charge. Since mass spectrometry is based on the mass to charge ratio of the ions, it makes sense that heavier ions may not react to alternating voltage
(Skoog, 2018, p.259). Heavier ions will be affected more so by the direct current voltage than the alternating current voltage (Skoog, 2018, p.259). If the ion has a low mass to charge ratio it is more likely to run into the negatively charged quadrupoles (Skoog, 2018, p.259). Thus, indicating that a high pass filter is being utilized by the positively charged quadruples for the positive ions within a sample (Skoog, 2018, p.259). The opposite is true if the quadrupoles are negatively charged. The negatively charged quadrupoles will act as a low pass filter.
There is a difference between a single quadrupole and a triple quadrupole mass spectrometer. A triple quadrupole mass spectrometer is usually referred to as tandem mass 22 spectrometry (MS/MS). A triple quadrupole can complete the analysis by being a tandem in space instrument. More specifically this means that two different analyses can take place in two different locations within the same mass spectrometer instrument. This also means that this type of instrument can use predetermined and fragmented ions as the reference ions when scanning an unknown sample (Skoog, 2018, p.524). This will only work if the analytes of interest are first optimized. The two main quadrupoles are quadrupole 1 (Q1) and quadrupole 3 (Q3).
Quadrupole 1 is referred to as a precursor ion scan. The sample will first encounter Q1 where the ions will be scanned and the second set of quadrupoles (Q3) will be held constant. The precursor ions that are filtered out will then move to the second set of quadrupoles (q2). This second stage is not actually an analysis stage. The lowercase q indicates that this stage does not actually contain any charged quadrupoles. This second step is referred to as the collision cell.
This is where the precursor ions are bombarded with an inert gas such as argon to promote further dissociation of the ions through collisional activated dissociation (Skoog, 2018, p.524).
An inert gas is used because it will not undergo ion transfer. This set of quadrupoles uses radio frequency current and no direct current voltage is applied (Skoog, 2018, p.525). Once the ions have dissociated into their product ions, they will be filtered again in the third set of quadrupoles
(Q3). This product ion spectrum is obtained by keeping Q1 constant. The fragmented ions then pass through an electron multiplier where they are then detected.
While this was not done for this research, another type of scan that can be run is called a neutral loss scan. This scan is performed by scanning Q1 and Q3 at the same time (Skoog, 2018, p.524). This will result in a mass difference between the precursor and the product fragment ions.
This scan will show which precursor fragments will experience losses due to certain common compounds such as 18 Daltons (Da) which is water. Other nonselective fragments include 60 Da 23 acetic acid, 46 Da formic acid, and 17 Da ammonia, just to name a few of the common loss sources (Berendsen, 2013). In order to account for neutral losses, the precursor ion mass will need to be changed during optimization. According to Berendsen et al., (2013), if a higher precursor ion mass is used then the neutral losses attributed to smaller molecular weight compounds such as water and ammonia should decrease. It is important to consider which compounds could result in a neutral loss when developing a method and when optimizing compounds.
1.8 Example of an Optimization
In the field of forensics, the analysts will be faced with the emergence of new compounds. These new compounds will need to be optimized and loaded into an existing method as the novel drugs become more popular. An example of how an analyst would take a known substance and incorporate it into an existing method will be described. The analyte of interest is psilocybin which is found in naturally occurring psilocybin mushrooms or “magic mushrooms”.
This analyte is classified as a hallucinogen. First, a standard and an internal standard of the analyte should be purchased. In this case, psilocybin was purchased from Cerilliant (Round
Rock, TX). The vial will have the concentration on the label. This will be important for the sample preparation for the optimization later. Also, on the label will be the Cerilliant product code, usually in the upper left-hand corner of the label. For psilocybin, its’ Cerilliant product code P-097. Next, go to the Cerilliant website https://www.cerilliant.com/ and type in that code in the ‘search catalog’ box. Then highlight the chemical formula and right click and copy the chemical formula. Next, find the website titled “Molecular Mass Calculator” by Christoph
Gohlke https://www.lfd.uci.edu/~gohlke/molmass/?q=. Then right click and paste the chemical formula from the Cerilliant website into the ‘formula:’ box. This website will give the 24 monoisotopic mass for the analyte of interest. For psilocybin, the monoisotopic mass is 284.0926
Da. This mass is representative of the most abundant isotopes in the analyte. The monoisotopic mass that the website gives, should be written down. Then in LabSolutions, open an existing method and click on the ‘MRM (+)’ tab. In the table under the column heading ‘Precursor m/z’ and the row heading ‘Ch.1’ type in the monoisotopic mass by adding one for positive mode and subtracting one for negative mode. For the psilocybin, the monoisotopic mass would be
285.0926 Da for positive mode and 283.0926 Da for negative mode. Add the positive mode monoisotopic mass first and click on the ‘positive’ circle located right above the ‘MRM (+)’ tab.
Then click on the ‘MRM (+)’ tab to enter the negative mode monoisotopic mass and click on the
‘negative’ circle above the ‘Product Ion Scan (+)’ tab. At this point if the analytical column is still connected in the LC oven, take it off and replace it with the union.
For the sample preparation, let the analyte come to room temperature assuming the analyte was stored at -20 degrees Celsius. Check the analyte concentration on the label. The starting concentration of the psilocybin is 1.0 mg/mL. A dilution will need to be performed in order to get a final concentration of 100 ng/ml in 50% mobile phase A and 50% mobile phase B.
The diluent will be methanol to start with. Next, vortex the vial to ensure equal mixing and even distribution of the analyte in the solution. This same procedure will be followed for the internal standard as well but keep in mind the starting concentration will be different on the label. Next, place the vial into the autosampler and record its vial position.
In LabSolutions turn on the LC-MS/MS to get the column to equilibrate to the optimal parameters. Also, in LabSolutions on the left-hand grey sidebar click on the ‘Optimization for
Method’ tab. Then select ‘Optimize MRM event from product ion search’ and click ‘next’.
Check the box ‘Optimize Voltage’ in the upper left-hand corner. Next, update the vial number so 25 that it corresponds to the vial position in the autosampler. Also, update the injection volume ‘Inj.
Vol.’. Click the ‘Apply to method file’ circle and click next. Then review the table and click
‘start’ if everything looks okay. This will also be done to ‘Optimize MRM event from the product ion search’ and to ‘Optimize voltage’. This optimizes the voltages for Q1, Q3, and q2 or collision energy (CE). The optimizations will need to be done twice, once for the positive mode and once for the negative mode. Once the optimizations are complete, review the data and make sure that the voltages that were picked are in the middle and at a high percentage. If the optimizations look good, then replace the union with the analytical column. Next, run a single injection twice using the parameters that were just optimized for a positive mode injection and a negative mode injection. Based on the results, either the positive mode or the negative mode should give better results and that mode should be used for subsequent data analysis for that analyte.
1.9 Internal Standards
Internal standards are labeled with stable isotopes that correspond to the analyte(s) of interest. An element has an isotope if there are more than one form of the element. An isotope contains the same number of protons but a different number of neutrons which contributes to the difference in mass between them. Internal standards that are exact matches to the standard analyte will have the exact same chemical structure except that the hydrogens or carbons, most likely, will be replaced with their stable isotopes (Stokvis, 2005). For hydrogen it will be deuterium denoted as 2H and for carbon it will be 13C (Stokvis, 2005). Usually anywhere from three to eight atoms will be replaced with their stable isotopes for an internal standard (Stokvis,
2005). The number of hydrogens that have been replaced with deuterium will be denoted by the number following the ‘D’ in the internal standard name. Hydrogen contains one electron and one 26 proton, while deuterium contains one electron, one proton, and one neutron (Britannica, 2020).
This difference in neutrons is responsible for the different masses of hydrogen (1.007 Da) and deuterium (2.014 Da) (Britannica, 2020). This difference in mass will aid in their separation.
The internal standard and the standard analyte should have the same or close to the same chemical structure. If the internal standard and the standard analyte do not have similar structures it could contribute to inaccurate quantitation due to differences in ion suppression between the two (Stokvis, 2005). The main difference between the two is that they will have different masses.
Ideally, there should be a minimum gap of three mass units between the internal standard and the standard analyte (Stokvis, 2005). This is to aid in the distinguishable elution between the two since they should essentially co-elute (Stokvis, 2005). The internal standard should elute a little before the standard analyte because of the deuterium (Stokvis, 2005). The deuterium experiences bonds with carbons that are stronger than the hydrogen-carbon bonds in the standard analyte
(Stokvis, 2005). This should also inhibit or decrease monoisotopic bleed through or otherwise known as “cross-talk” from the standard analyte (Stokvis, 2005). If there is not at least three units of mass between the two, then the standard analyte peaks could interfere with the internal standard due to the isotopic atoms in the standard analyte causing the monoisotopic bleed through (Stokvis, 2005). This was observed between the standard analyte Lorazepam and its’ internal standard Lorazepam D4.
Lorazepam is a benzodiazepine and has a monoisotopic mass of 320.0119 Da from
Cerilliant. The lorazepam D4 has a monoisotopic mass of 324.0370 Da from Cerilliant. That is a difference of 4.0251 Da between the standard analyte and the internal standard. Because the difference was greater than 3 Da, no cross-talk or “monoisotopic bleed” should have been observed. Lorazepam contains two halogenated chlorine atoms which are isotopes. They consist 27
of 35Cl and a 37Cl. What was observed was that the standard analyte contained two 37Cl atoms
instead of two 35Cl atoms which mimicked the monoisotopic mass of the lorazepam D4 internal
standard. This created the monoisotopic bleed through from the standard affecting the internal
standard signal resulting in a non-linearity.
The lorazepam monoisotopic bleed through or otherwise known as cross-talk was tested
and confirmed by running a 100 ng/mL concentration of the lorazepam standard in methanol
without the addition of the ISTD solution. The lorazepam linearity was normalized without the
addition of the lorazepam ISTD. The results were then compared to the sample that contained
both lorazepam the ISTD solution. It was observed that the lorazepam -D4 ion area counts were high relative to the other ISTDs in the sample. For example, the ion area counts for diazepam -
D5, which is also a benzodiazepine, within the same ISTD solution, had a range of ion area counts from 336,400 to 391,519. Whereas, lorazepam-D4 had a range of ion area counts from
482,537 to 845,590. This confirmed that the lorazepam standard analyte was contributing to the lorazepam-D4 signal enhancement due to cross-talk.
In order to fix this issue, either more of the lorazepam internal standard should be added to the sample or a different stable isotopic labeled mass of the internal standard needs to be used instead of the D4. In this case rather than adding more lorazepam internal standard, a different monoisotopic mass was used such as lorazepam D6 of 326.0120 Da. This change reduced the lorazepam contribution by about 60-fold and still was able to retain about 80% of the intensity.
This information was obtained from the Cerilliant and Molecular Mass Calculator by Christoph
Gohlke websites.
It is not necessary or feasible in some cases to have an internal standard for every standard analyte being analyzed. Sometimes internal standards are not available for certain 28 standard analytes, especially for novel analytes (Stokvis, 2005). If the internal standard and the standard are structurally similar, processed in the same mode (positive or negative), and have similar retention times then internal standards can be shared between analytes. In order to correctly match an internal standard with a standard analyte, a trial and error process may be used.
Internal standards are used for several reasons. One reason is that calibration curves are typically made using organic solvents which behave differently than human blood or any other matrix being studied. This is referred to as matrix mismatch (Bell, 2014, p.66 & 590). Artificial serum can be used to combat matrix mismatch to an extent. Because the serum is artificial it does not exactly mimic human blood. By using artificial serum, it will be closer to the real matrix than if methanol was used to prepare a calibration curve. Also, the nature of the matrix, whether artificial or biological, could influence the standard analyte signal. An example of this would be ion suppression resulting in a lower response signal (Liang, 2003). This ion suppression could result from the multiple interferants within blood such as proteins and phospholipids that were not successfully precipitated and filtered out from the blood during the SPE process. As described in the SPE section, these biological interferants could coelute with the standard analytes and cause the ion suppression. In order to account for matrix mismatch and matrix effects the use of internal standards is recommended (Bell, 2014, p.66; Liang, 2003; Stokvis,
2005). Internal standards can correct for matrix effects and matrix mismatching only if the internal standard experiences the same method parameters that the standard analyte experiences.
This is because the ion suppression and, the differences between the organic solvents and the biological matrix as in, matrix mismatching will not only affect the standard analytes by a certain 29 amount, but the ion suppression and matrix mismatching will also affect the internal standard by that same amount. This will in effect normalize the standard analyte signal.
Internal standards are also used to normalize the signal given by the standard analyte to account for analyte loss (Liang, 2003). Internal standards are used for quantitation purposes
(Stokvis, 2005). More specifically the internal standards will act as a correction factor for errors, when they arise, in the detection process (Stokvis, 2005). The internal standard acts as a reference that the signal from the standard can be compared to and ratioed, which in effect will normalize the standards’ signal (Bell, 2014, p.66). The word signal relates to the concentration of the standard to the internal standard. The normalization between the standard and internal standard accounts for errors in the sample preparation because the internal standard usually goes through the same sample preparation that the sample goes through (Bell, 2014, p.66; Stokvis,
2005). However, this is only true if the ISTD is added at the beginning of the sample preparation such as with the addition of caffeine in the oral wash solution that was used for the other research validation project that was run in conjunction with this validation project. The caffeine was added as an internal tracer in order to back calculate the amount of OF that was collected. For this validation project, where blood was used the ISTDs were added at the end right before the samples were injected into the LC-MS/MS. This normalization between the standard and the internal standard can account for differences in sample injections because the internal standard and the standard analytes are in the same vial (Liang, 2003; Stokvis, 2005). If the sample injections experience any variations, then the internal standard will reflect that as well as the standards and the signal between the two will be normalized. This is also the case with any variations related to the instrumental parameters because the standard and the internal standard will undergo the same conditions throughout the data acquisition period (Liang, 2003; Stokvis, 30
2005). That is why internal standards are preferred when quantifying analytes of interest within a biological sample. 31
CHAPTER 2. RESEARCH FOCUS
The long-term goal of this research was to validate a method to detect and quantify illicit
drug use in blood utilizing SPE and LC/MS/MS following the guidelines outlined in the Clinical
Lab Consulting Method Validation Protocol. The overall objective of this research was to
evaluate multiple parameters such as: linearity or the reportable range, limit of detection (LOD) or analytical sensitivity, precision, analytical accuracy, patient correlation using human blood
samples, carryover, interference, and matrix effects for the analysis of illicit drug use in blood.
The central hypothesis is that the use of the SPE will be an effective and efficient process at
separating out phospholipids and proteins for the analysis of blood. This hypothesis is based on
literature comparing existing SPE methods with this new SPE method. The rationale is that once
these new methods, for detecting illicit drug use in blood, have become validated they can be
used as a guideline for other forensic science laboratories to do their own validation studies. The
central hypothesis was tested using the following specific aim.
Specific Aim: Validate SPE coupled with LC/MS/MS methods for the detection and
quantitation of illicit drug use in blood utilizing the guidelines from Clinical Lab
Consulting Method Validation Protocol. 32
CHAPTER 3. MATERIALS AND EXPERIMENTAL DESIGN 3.1 Materials The Cerilliant (Round Rock, TX) 82 standard analytes and the 39 internal standards were
purchased from Sigma-Aldrich (St. Louis, MO) found in the appendix. Mobile phase A consisted
of 0.1% acetic acid (100% LC-MS grade) purchased from Millipore Sigma (Darmstadt,
Germany) and water (LC-MS grade) purchased from Fisher Chemical (Waltham, MA). Mobile
phase B consisted of pure methanol (LC-MS grade) from Fisher Chemical (Waltham, MA).
Cleaning reagents used for the bakeouts included: acetonitrile (HPLC grade plus > 99.9%) and 2- propanol (HPLC grade 99.9%) purchased from Sigma-Aldrich (St. Louis, MO). Formic acid
(Mass Spectrometry grade ~98%) was purchased from Fluka Analytical (Honeywell, Charlotte,
NC). The artificial serum that was used was a buffered recombinant human serum albumin and
(SigMatrix Serum Diluent) was purchased from Sigma-Aldrich (St. Louis, MO). The compressed
5.0 ultra-high purity (UHP) grade argon gas tank was purchased from Praxair (Danbury, CT).
The two types of nitrogen gas tanks that were used for the evaporation step included: a liquid
nitrogen tank (230 Pounds per Square Inch PSI) and a nitrogen compressed gas tank both
obtained from Praxair (Danbury, CT). A nitrogen generator was used supply the heating, drying
and nebulizing gases from Proton OnSite (Wallingford, CT).
The equipment and instruments that were used for this research includes the following
from the sample preparation, SPE cleanup, and analysis. For the sample preparation, Eppendorf
Research Plus pipettes (Hauppauge, NY), 0.5-10 µL, 10-100 µL, 20-200 µL, and 100-1,000 µL
were used along with their corresponding tips (0.1-10 µL Non-sterilized natural tips) and (1-200
µL Non-sterilized yellow tips) from Fisherbrand (Fisher Chemical, Walthman, MA). The pipet
tips (1,000 µL polypropylene blue tips) were purchased from Labcon (Petaluma, CA). Glass 33 amber 2 mL vials and their corresponding 9 mm red screw caps (PTFE 1 mm thick septa, non- slit) were purchased from Leap Pal Parts (Raleigh, NC). Certified glass vial inserts (Mandrel
Precision Point 200 µL, 6X29 MM with plastic spring) were purchased from Sigma-Aldrich (St.
Louis, MO).
The SPE clean-up was done using Supelco’s (Bellefonte, PA) HybridSPE-Phospholipid
Technology purchased from Sigma-Aldrich (St. Louis, MO). This included the Visiprep vacuum manifold and SPE Vacuum Pump Trap Kit from Supelco (Bellefonte, PA) and Labport (Trenton,
NJ) Mini Laboratory Pumps. An IKA (Wilmington, NC) Vortex 3 was used on a setting of 4.
The disposable flow control valve liners for the Visiprep and the HybridSPE-Phospholipid
Cartridges were obtained from Sigma-Aldrich (St. Louis, MO). VWR (Radnor, PA) disposable culture test tubes size 10X75 mm and VWR (Radnor, PA) disposable Pasteur pipets size 9” were obtained from Bowling Green State University’s Biology stockroom (Life Science Building,
Room 217).
For the analysis, the LC-MS/MS work was done using a Shimadzu (Canby, OR) 8050
Triple Quadrupole instrument. For the stationary phase, a Raptor biphenyl column (50 mm X 2.1 mm, 2.7 µL and a force biphenyl 5 µm guard column were used from Restek (Bellefonte, PA).
3.2 Experimental Design
First, stock solutions were made for the calibration curve (CCSS) at a concentration of 50 ng/mL, high quality control (HiQC_SS) at a concentration of 25 ng/mL, low quality control
(LoQC_SS) at a concentration of 0.6 ng/mL and internal standard (ISTD_SS). The CCSS,
HiQC_SS, and LoQC_SS are good for one year if they are stored in the freezer (-20ºC). The
ISTD_SS is good for two months if stored in the freezer (-20ºC). The stock solutions were made using either a 100 mL or 10 mL volumetric flask, depending on the final concentration needed, 34 as indicated on the prep guides in the appendix in the respective order as listed above. Each of the analytes were aliquoted to the volumetric flask at their specific volumes and then methanol was added to bring the final volume up to the line on the volumetric flask. Para film covered the top of the volumetric flask and it was inverted several times to ensure equal mixing between the analytes and the methanol. The para film was removed, and the stock solution was aliquoted using a disposable glass Pasteur pipet to a clean, labeled amber bottle with a cap, and placed into the freezer at -20ºC for storage. This same procedure was repeated for each of the stock solutions.
Next, a calibration curve was prepared using the stock solutions and were diluted using methanol. The prep guide in the appendix ‘Calibration Curve Stock (CCS) and QC Stock (QCS)
Preparation’ for the methanol stocks was followed. The methanol stocks are good for two months if stored in the freezer at -20ºC. Next, the methanol stocks were vortexed and further diluted in the artificial serum as shown in the prep guide ‘Artificial Serum (AS) Calibrators and
Controls Preparation’ in the appendix and are good for 48 hours if stored in the freezer at -20ºC.
For this validation study the artificial serum calibrators and QCs were vortexed and prepared fresh each day along with two sets of QCs.
A precipitating agent or “crash solution” was made, in the fume hood, using 99 mL of acetonitrile and 1 mL of formic acid. The “crash solution” was stored in the freezer at -20ºC before each use. In the fume hood, 600 µL of the “crash solution” was added to a new set of amber vials that were appropriately labeled corresponding to the calibrators and QCs. Then 200
µL of the artificial serum calibrators and QCs were added to the 600 µL of the “crash solution” in their respective vials. Each vial was then vortexed on a setting of 4 for five minutes. This 35 created a visible precipitation of the proteins at the bottom of the vial and the liquid sample on the top.
For the SPE process, clean disposable flow control valve liners were inserted into the
Visiprep manifold and the valves were closed tightly. Clean, labeled test tubes were placed into the bottom of the Visiprep manifold. The HybridSPE-Phospholipid cartridges were labeled and placed directly above the corresponding test tubes. The supernatant was aliquoted from the vial containing the artificial serum and the “crash solution” to the HybridSPE-Phospholipid cartridge, using a new disposable glass pipet for each sample. Care was taken to not draw up any of the protein precipitate. Once every calibrator and QC was aliquoted to their respective HybridSPE-
Phospholipid cartridge, the pump was turned on and allowed to build a pressure of 15” Hg. Next, the sample valves were fully opened allowing the sample to flow through the HybridSPE-
Phospholipid cartridge and the phospholipids to be retained on the cartridge. Once all of the liquid in the samples was in the test tubes, and out of the HybridSPE-Phospholipid cartridge, the pump was turned off and the main pressure valve was opened to allow the Visiprep manifold to depressurize so the lid could be easily removed. The test tubes were placed into a test tube rack and the used HybridSPE-Phospholipid cartridges and disposable flow control valve liners were discarded into the biohazardous bin. The plastic flow control valves were removed and cleaned with warm water and Dawn dish soap to prevent sample cross-contamination for the next use.
Next, the filtered samples were placed into the fume hood for the evaporation process.
Either the gas off a liquid nitrogen tank was used or a nitrogen gas tank was used for this step. A steady flow of nitrogen gas was used at a low flow rate over the test tube, to prevent splashing up the side of the test tube, to evaporate off most of the acetonitrile from the “crash solution”. The sample in the test tube was exposed to nitrogen gas for approximately 5 to 10 minutes or until the 36 volume was reduced to approximately 100 µL. A test tube was filled with 100 µL water as a guide.
Next, the ISTD_SS was removed from the freezer, vortexed and set aside to come to room temperature. Then, 30 µL of ISTD_SS was aliquoted to each calibrator and QC test tube.
The test tubes were then vortexed to resuspend any standard analyte that may have stuck to the side of the glass of the test tube during the evaporation process. Next, a glass vial insert was inserted into the final set of labeled glass amber vials. The liquid sample in the test tubes were then aliquoted using a new disposable glass Pasteur pipet for each sample into their corresponding amber vial insert. The samples were then placed into the autosampler in the LC with their corresponding vial position recorded in the batch file as indicated in the ‘Batch File
Example’ located in the appendix.
For the analysis of the calibrators and QCs, the LC-MS/MS was operated under the same conditions. Mobile phase A consisted of a 4 L bottle of LC-MS grade water with 4 mL of acetic acid added to the water to get a 0.1% acetic acid in water solvent and mobile phase B consisted of 4 L bottle of pure methanol. The stationary phase consisted of a Raptor biphenyl column with a force biphenyl guard column, to protect the analytical column by acting as a filter. A gradient method was utilized consisting of a flow rate that was a constant 0.75 mL/min at 70°C over the
6.75 min gradient between 1% and 100% MeOH with a total method run time of 10 minutes. The needle depth in the autosampler was set at 47 mm. The mass spectrometer parameters consisted of the nebulizing gas flow rate of 3 L/min, heating gas flow rate of 20 L/min, drying gas flow rate of 10 L/min, interface temperature of 400ºC, DL temperature of 215ºC, and heat block temperature of 450ºC. The standard analytes and internal standards had been optimized on the mass spectrometer before the start of this validation study. The optimizations were done the 37 same way as described in section ‘1.8 An Example of an Optimization’. The data processing parameters are shown in the appendix, ‘Standard Analyte Method Parameters’ and ‘Internal
Standard Method Parameters’.
For the batch file (see the appendix), a total of six null injections were run first to equilibrate the biphenyl column to the optimal method parameters. Then four methanol injections were performed to clean the column from any residues that may have been on the column from previous runs to prevent carryover. Then the calibrators and QCs were injected back to back in the order of 0.40 ng/mL, 0.75 ng/mL, CO (cutoff 1ng/mL), 2.0 ng/mL, 3.0 ng/mL, 10.0 ng/mL, 50 ng/mL, 100 ng/mL, NegQC1, 30% CO (0.3 ng/mL), LoQC1, SST
(system suitability test, 1.0 ng/mL), HiQC1, NegQC2, LoQC2, and HiQC2 with 3µL injections.
Next, four more methanol injections are performed to clean the column in between the two calibration curve injections. The same calibration curve was injected again in the exact same order followed by four more methanol injections to clean the column at the end of the batch.
For the parameters that were analyzed in this research such as the linearity or reportable range, limit of detection or analytical sensitivity, and precision, the data was read in Insight at the completion of the batch, and if the peaks for the chromatography looked symmetrical and the R2 values were in the 0.95 range or higher, then the same batch file was repeated in order to get two batches run within 24 hours of the time the calibration curve and QCs were initially prepared.
This was repeated until five days’ worth of good quality data was obtained. The mass spectrometer ESI source was cleaned in between every batch. The ESI source was cleaned using
LC-MS grade water poured onto a Kimwipe followed by methanol poured onto a Kimwipe.
Based on multiple previous batch runs with artificial serum it was determined that the
ESI source needed to be more thoroughly cleaned in between each day of runs. This was done to 38 ensure that sensitivity would not be lost due to the matrix of the samples contaminating the source. An abbreviated 2-hour acetonitrile (ACN) bakeout consisted of using acetonitrile, which is a stronger organic than methanol, to essentially “bakeout” the source. The two mobile phase lines (A and B) were placed into a bottle of acetonitrile and the pumps were purged three times to clear the lines of any mobile phase and to get the ACN running through the lines. Next, the
ACN method was downloaded, where all the gases were turned off except for the nebulizing gas which was turned down from 3.0 L/min to 1.5 L/min. The pumps were also turned on at 50% in order to get the ACN through both solvent lines and to the mass spectrometer ESI source. The desolvation line (DL) and the heat block temperatures were manually turned from 300 and 500 degrees, respectively, to 90 degrees each in a stepwise manner by 100º. This was done in steps to eliminate any heater errors that may have occurred otherwise. Once the temperatures reached 90 degrees, the ACN was allowed to flow at a rate of 0.25 ml/min for 25 minutes in order to coat the
ESI source. After the 25 minutes, the DL and heat block temperatures were manually turned back up to 300 and 500 respectively in 100º increments. A batch file was setup in order to ensure that the pumps would not time out during the abbreviated 2-hour bakeout. The oven was set at a temperature of 40ºC. A more thorough ACN bakeout was done at the end of the week (5 days’ worth of runs) for 10 hours. If feasible, an abbreviated 2-hour bakeout should be done in between every batch run.
Once the 2- or 10-hour ACN bakeout was complete the mobile phase solvent lines needed to be flushed. First, the solvent lines needed to be removed from the bottle of ACN and wiped down with a Kimwipe. The red PEEK tubing that connects the LC to the ESI needed to be removed and placed into a waste beaker. Next, the solvent lines were placed into a bottle of LC-
MS grade water. The pumps were purged three times to eliminate the ACN from the lines and to 39 flush the lines with the water. The pumps were turned back on and the water flowed at a rate of
0.7 ml/min for 30 minutes.
Next, the solvent lines were flushed with isopropyl alcohol (a.k.a IPA or 2-propanol).
The solvent lines were removed from the bottle of water and were wiped down with a Kimwipe and placed into the bottle of IPA. The pumps were purged three times. The flow rate was adjusted to 0.35 ml/min to keep the pump pressures within the range of ~2500-3500 psi for 30 minutes. IPA is normally only used if there are bubbles in the mobile solvent lines and caution should be used because IPA can cause high back pressure within the column.
In order to get the mobile phases back into their respective solvent lines, a beaker of mobile phase A (0.1% acetic acid in water) and a beaker of mobile phase B (pure methanol) was used. The corresponding solvent lines were wiped off with a Kimwipe and then placed into the correct beaker with their appropriate mobile phase. The pumps were purged three times. Next, the solvent lines were removed from the beakers and placed back into their respective mobile phase bottles (4L bottles). Para film was used to seal or cover the section of the bottle where the cap would not screw on tightly to prevent evaporation and contamination. The pumps were then purged several times to ensure no bubbles were trapped inside of the solvent lines. The pumps were turned on and the mobile phases were run under the same conditions at the water and the
IPA for 30 minutes. After the 30 minutes the pumps were turned off and the red PEEK tubing was reconnected to the ESI. Since the analytical column was left on during the bakeout, it is recommended that four methanol injection be added to the batch file before any samples are injected. This was taken care of with the four methanol injections following the six null injections before the calibrators were injected. This process was repeated in between every days’ 40 runs. Once, the five days’ worth of good quality data was obtained, analysis of variance
(ANOVA) statistics were run on the data. 41
CHAPTER 4. RESULTS 4.1 Linearity/Reportable Range The linearity/reportable range results are listed in Table 1. The linearity/reportable range results show a range of concentrations that fall between the cutoff concentration which was 1 ng/mL and the upper limit concentration of the calibration curve which was 100 ng/mL. As previously mentioned, 82 analytes were used in this validation study. However, amobarbital and pentobarbital are positional isomers meaning that they co-elute because the only difference from one compound to the other is where the methyl group is bound, therefore they are classified as one analyte (Lupo, 2016). Two HiQCs were prepared fresh each day. They were labeled HiQC 1 and HiQC 2 and were run in duplicate for two runs within a 24-hour period. The results from each run were analyzed and manually integrated if needed. The necessary information was then extracted from a computer program (Postrun) and placed into an Excel spreadsheet in order for statistical analysis to be performed.
The linearity/reportable range results in Table 1 are based off one of the HiQCs that was representative of most of the data. However, any of the calibrators or QCs could have been used because they are all referencing the same calibration curve and should have the same R2 values.
If they do not, then the data in Insight has not been properly integrated or reviewed. In this case, the HiQC was selected each time for consistency purposes. The 81 analytes are listed in the table along with their corresponding average R2 values. The coefficient of variance percentage (CV%) was calculated using the raw data from Postrun. The minimum and maximum R2 values were also determined using a Min and Max function in Excel on the raw data. The red highlighted R2 values do not meet the minimum R2 value requirement of 0.950. 42
Table 1: Linearity/Reportable Range Results
# Analyte Average R2 CV% Min R2 Max R2 10,11-Dihydro-10- 1 Hydroxycarbamazepine 0.992 0.4% 0.986 0.997 2 2-Hydroxyethylflurazepam 0.993 0.3% 0.989 0.997 3 4-Methylephedrine 0.995 0.4% 0.986 0.999 4 6-MAC 0.993 0.2% 0.991 0.997 5 6-MAM 0.995 0.3% 0.990 1.000 6 7-Aminoclonazepam 0.993 0.2% 0.988 0.996 7 Alfentanil 0.995 0.3% 0.991 0.999 8 Alprazolam 0.994 0.3% 0.991 0.999 9 Amo-Pentobarbital 0.980 1.4% 0.950 0.996 10 Amphetamine 0.988 1.0% 0.963 0.998 11 Benzoylecgonine 0.991 0.4% 0.984 0.996 12 Buprenorphine 0.994 0.2% 0.990 0.999 13 Bupropion 0.992 0.4% 0.984 0.997 14 Butabarbital 0.973 1.0% 0.955 0.990 15 Butalbital 0.972 1.3% 0.950 0.990 16 Caffeine 0.974 2.9% 0.903 0.996 17 Cannabidiol 0.932 5.0% 0.841 0.978 18 Carisoprodol 0.993 0.4% 0.985 0.999 19 Clonazepam 0.995 0.2% 0.991 0.999 20 Cocaethylene 0.995 0.3% 0.991 0.999 21 Cocaine 0.995 0.3% 0.990 0.999 22 Codeine 0.989 0.4% 0.984 0.995 23 Cotinine 0.993 0.5% 0.983 0.999 24 Desalkylflurazepam 0.995 0.3% 0.988 0.998 25 Desmethyltapentadol 0.995 0.3% 0.990 0.999 26 Dextromethorphan 0.995 0.3% 0.991 0.999 27 Dextrorphan 0.995 0.3% 0.991 0.999 28 Diazepam 0.994 0.3% 0.990 0.999 29 Dihydrocodeine 0.993 0.3% 0.987 0.997 30 Ecgonine methyl ester 0.993 0.6% 0.981 0.998 31 EDDP 0.995 0.3% 0.991 0.999 32 Ephedrine 0.989 0.7% 0.978 0.999 33 Fentanyl 0.994 0.3% 0.991 0.999 34 Flurazepam 0.995 0.4% 0.989 0.999 35 Gabapentin 0.985 0.7% 0.971 0.993 36 Hydrocodone 0.991 0.8% 0.973 0.998 37 Hydromorphone 0.992 0.8% 0.976 0.998 43
38 Hydroxyalprazolam 0.992 0.5% 0.983 0.999 39 Hydroxymidazolam 0.994 0.3% 0.990 0.998 40 Hydroxytriazolam 0.992 0.4% 0.987 0.998 41 Ketamine 0.994 0.3% 0.990 0.998 42 Lorazepam 0.994 0.3% 0.990 0.998 43 MDA 0.995 0.3% 0.989 0.999 44 MDMA 0.995 0.3% 0.990 0.999 45 MDPV 0.991 0.5% 0.980 0.996 46 Meperidine 0.995 0.3% 0.988 0.999 47 Meprobamate 0.986 0.6% 0.978 0.999 48 Methadone 0.995 0.3% 0.990 0.999 49 Methamphetamine 0.995 0.3% 0.990 0.999 50 Methylone 0.994 0.3% 0.990 0.998 51 Midazolam 0.995 0.4% 0.988 0.999 52 Morphine 0.990 0.4% 0.983 0.996 53 Nalbuphine 0.993 0.4% 0.984 0.999 54 Norbuprenorphine 0.990 0.4% 0.985 0.997 55 Norcodeine 0.993 0.5% 0.980 0.999 56 Nordiazepam 0.995 0.2% 0.991 0.998 57 Norfentanyl 0.995 0.3% 0.990 0.999 58 Norhydrocodone 0.993 0.8% 0.975 0.999 59 Norhydromorphone 0.989 0.9% 0.972 0.997 60 Norketamine 0.994 0.4% 0.988 0.999 61 Normeperidine 0.996 0.3% 0.990 1.000 62 Noroxycodone 0.994 0.4% 0.987 0.998 63 O-Desmethyl-cis-Tramadol 0.993 0.6% 0.981 0.998 64 Oxazepam 0.993 0.3% 0.987 0.998 65 Oxycodone 0.994 0.4% 0.987 0.998 66 Oxymorphone 0.994 0.4% 0.986 0.998 67 PCP 0.995 0.3% 0.991 0.999 68 Pentazocine 0.995 0.4% 0.985 0.999 69 Phenobarbital 0.954 4.4% 0.842 0.995 70 Phentermine 0.993 0.4% 0.984 0.998 71 Pregabalin 0.987 0.7% 0.973 0.996 72 Secobarbital 0.981 0.9% 0.959 0.994 73 Sufentanil 0.995 0.3% 0.990 0.999 74 Tapentadol 0.995 0.3% 0.990 0.999 75 Temazepam 0.995 0.2% 0.992 0.998 76 THC 0.975 2.4% 0.934 0.996 77 THC-COOH 0.964 3.0% 0.884 0.988 44
78 THC-OH 0.967 1.9% 0.922 0.992 79 Tramadol 0.995 0.4% 0.989 0.999 80 Triazolam 0.994 0.3% 0.990 0.998 81 Zaleplon 0.995 0.3% 0.990 0.998
First, the data from Insight had to be applied to the same data in Postrun. This was done
by saving the changes that were made in Insight such as dropping calibrators and manually
integrating peaks that had not been integrated by the computer program. Next, by opening that
same data file in Postrun and loading the instrument parameters the changes that were made in
Insight were automatically applied to the data in Postrun.
The first HiQC that was made fresh each day for the remaining five days of analysis was
used. A summary Excel table was used to place all of the R2 values from each batch run. This
included R2 values for a total of 10 runs because each HiQC was run twice a day. The R2 values were then averaged for each analyte which is depicted in Table 1. The standard deviation of the population was also calculated for the 10 R2 values for each analyte using the standard deviation
of a population function in Excel. The CV% was calculated by dividing the standard deviation of
the population (SDP) by the average R2 (Avg R2) values for each analyte (SWGTOX, 2013;
Gibbs, 2019). Once these calculations were performed in the summary table then a more
condensed table was put together and is labeled as Table 1.
% =
𝑆𝑆𝑆𝑆𝑆𝑆 𝐶𝐶𝐶𝐶 2 The R2 values should have been greater than𝐴𝐴𝐴𝐴𝐴𝐴 or𝑅𝑅 equal to 0.950 (Gibbs, 2019). While most
of the analytes met this requirement, a few did not. For example, caffeine and butalbital had a
significant outlier for one of the runs on one of the days, so those two points were excluded in
order to not skew the data and to give a more accurate representation of the data. The significant
outliers were 0.528 for butalbital and 0.048 for caffeine. The averages for those two consisted of 45
a total of 9 values instead of 10 like the rest of the analytes. Other problematic analytes included
cannabidiol, phenobarbital, THC, THC-COOH and THC-OH. It should also be noted that for
THC the weighting was consistently changed for each run from the default setting of 1/C2 to 1/C in order to improve the R2 value. Analytes that fall into the drug class of barbiturates such as amo-pentobarbital, butabarbital, butalbital, phenobarbital, and secobarbital had to have at least one but not more than four of their 16 calibrators dropped due to poor chromatography. This was
done to improve their R2 value. For most of the other analytes, their R2 values were consistently
greater than or equal to 0.950 and none of their calibrators had to be dropped. Another important
validation parameter that was also extracted from the five days’ worth of data was the limit of
detection and limit of quantitation.
4.2 Limit of Detection The limit of detection results are shown in Table 2. There is a difference between the
limit of detection (LOD) and the limit of quantitation (LOQ). The limit of detection is defined as
the smallest concentration of an analyte that the LC-MS/MS can detect apart from background
(ICH Q8, 2005)). This would be important for the qualitative analysis or detection of the
presence of a drug in a blood sample as is the case in forensic science. The limit of quantitation
is defined as the lowest concentration of a drug that can be quantified (ICH Q8, 2005). The
quantitation of drugs in blood is gaining importance. For example, as marijuana becomes
recreationally legal in more states the need to be able to quantitate the amount of a drug in a
person’s blood sample is imperative. The LOQ is important when determining which analytical
instrument will meet the needs of the analyst. It is also important when determining what the
cutoff concentration should be for each analyte. This cutoff concentration will be the determining
factor on whether or not a person’s blood sample contains a drug concentration at or above this
cutoff concentration for a positive result or below the cutoff concentration for a negative result 46 for that drug (Gibbs, 2019). This is true for clinical toxicology labs; however, for forensics as of now the detection or presence which is anything above zero is considered a positive result. In the clinical setting if the concentration is below the cutoff concentration, then it is referred to as ‘not detected’ by the analytical instrument (Gibbs, 2019). The cutoff concentration is defined as three times the signal to noise ratio and the cutoff concentrations for each analyte should be at least be equal to or greater than the LOD and LOQ values for their respective analytes (ICH Q8, 2005).
The condensed LOQ data is shown for each of the analytes in Table 2. The Cutoff/LOQ ratio should be equal to or greater than 2.0, so any ratio that is not 2.0 or greater is highlighted in red. 47
Table 2: Limit of Detection Results LOQ Max Min # Analyte Average Cutoff LOQ/Cutoff CV% Ratio Ratio 10,11-Dihydro-10- 1 0.128 1 7.8 25.1% 11.7 5.7 Hydroxycarbamazepine 2 2-Hydroxyethylflurazepam 0.123 1 8.2 24.7% 13.1 6.3 3 4-Methylephedrine 0.100 1 10.0 42.6% 24.3 5.6 4 6-MAC 0.122 1 8.2 14.4% 11.9 6.9 5 6-MAM 0.094 1 10.6 40.6% 31.0 6.7 6 7-Aminoclonazepam 0.127 1 7.9 16.9% 10.5 6.0 7 Alfentanil 0.097 1 10.3 39.7% 22.6 7.0 8 Alprazolam 0.111 1 9.0 27.6% 17.3 6.8 9 Amo-Pentobarbital 0.239 1 4.2 39.5% 9.1 2.3 10 Amphetamine 0.161 1 6.2 47.4% 16.0 2.9 11 Benzoylecgonine 0.141 1 7.1 21.1% 11.0 5.2 12 Buprenorphine 0.113 1 8.8 21.3% 17.6 6.7 13 Bupropion 0.128 1 7.8 22.8% 11.6 5.2 14 Butabarbital 0.292 1 3.4 22.9% 6.2 2.7 15 Butalbital 0.460 1 2.2 99.3% 5.3 0.6 16 Caffeine 0.983 1 1.0 224.5% 8.8 0.1 17 Cannabidiol 0.491 1 2.0 44.9% 3.8 1.2 18 Carisoprodol 0.125 1 8.0 29.5% 18.3 5.4 19 Clonazepam 0.107 1 9.4 27.2% 25.2 7.1 20 Cocaethylene 0.096 1 10.4 37.3% 25.8 7.0 21 Cocaine 0.101 1 9.9 35.8% 20.2 6.5 22 Codeine 0.156 1 6.4 19.1% 9.4 5.2 23 Cotinine 0.119 1 8.4 43.6% 24.1 5.0 24 Desalkylflurazepam 0.107 1 9.3 29.1% 15.9 6.1 25 Desmethyltapentadol 0.095 1 10.5 43.2% 27.6 6.7 26 Dextromethorphan 0.118 1 8.5 57.9% 24.0 3.5 27 Dextrorphan 0.100 1 10.0 33.4% 17.6 7.0 28 Diazepam 0.114 1 8.8 29.2% 22.3 6.8 29 Dihydrocodeine 0.126 1 8.0 21.1% 12.2 5.8 30 Ecgonine methyl ester 0.113 1 8.9 41.4% 15.0 4.7 31 EDDP 0.098 1 10.2 35.1% 21.1 7.2 32 Ephedrine 0.152 1 6.6 33.6% 18.3 4.4 33 Fentanyl 0.106 1 9.5 35.6% 25.2 6.9 48
34 Flurazepam 0.097 1 10.3 45.2% 25.5 6.4 35 Gabapentin 0.193 1 5.2 25.8% 7.7 3.6 36 Hydrocodone 0.129 1 7.7 44.9% 16.2 4.0 37 Hydromorphone 0.121 1 8.3 48.4% 16.2 4.2 38 Hydroxyalprazolam 0.128 1 7.8 36.9% 20.7 5.0 39 Hydroxymidazolam 0.116 1 8.6 25.4% 15.4 6.6 40 Hydroxytriazolam 0.131 1 7.7 24.6% 14.9 5.7 41 Ketamine 0.109 1 9.2 31.9% 17.0 6.5 42 Lorazepam 0.108 1 9.3 30.1% 14.3 6.5 43 MDA 0.100 1 10.0 34.9% 17.7 6.4 44 MDMA 0.101 1 9.9 34.0% 19.5 6.6 45 MDPV 0.137 1 7.3 25.7% 10.6 4.6 46 Meperidine 0.102 1 9.8 34.1% 17.7 6.1 47 Meprobamate 0.175 1 5.7 27.3% 17.9 4.2 48 Methadone 0.098 1 10.2 40.7% 25.8 6.7 49 Methamphetamine 0.102 1 9.8 37.4% 25.7 6.7 50 Methylone 0.115 1 8.7 24.6% 14.2 6.6 51 Midazolam 0.102 1 9.8 40.2% 23.3 6.0 52 Morphine 0.161 1 6.2 22.5% 9.3 4.3 53 Nalbuphine 0.118 1 8.5 29.4% 17.4 5.3 54 Norbuprenorphine 0.148 1 6.8 23.2% 11.8 5.3 55 Norcodeine 0.118 1 8.4 35.9% 19.5 4.7 56 Nordiazepam 0.101 1 9.9 27.7% 16.6 7.0 57 Norfentanyl 0.101 1 9.9 38.0% 22.1 6.7 58 Norhydrocodone 0.116 1 8.6 50.7% 19.2 4.2 59 Norhydromorphone 0.145 1 6.9 39.7% 12.9 3.9 60 Norketamine 0.109 1 9.2 38.2% 17.9 6.1 61 Normeperidine 0.092 1 10.9 39.8% 32.0 6.8 62 Noroxycodone 0.112 1 8.9 36.1% 15.6 5.7 O-Desmethyl-cis- 63 0.115 1 8.7 42.3% 15.4 4.8 Tramadol 64 Oxazepam 0.120 1 8.3 25.7% 13.7 5.8 65 Oxycodone 0.108 1 9.3 33.9% 16.6 5.9 66 Oxymorphone 0.116 1 8.6 29.8% 16.0 5.5 67 PCP 0.100 1 10.0 31.1% 20.7 7.1 68 Pentazocine 0.102 1 9.8 35.5% 18.3 5.5 69 Phenobarbital 0.386 1 2.6 46.9% 7.8 1.2 70 Phentermine 0.128 1 7.8 30.8% 16.3 4.9 49
71 Pregabalin 0.188 1 5.3 31.5% 9.4 3.2 72 Secobarbital 0.234 1 4.3 24.6% 8.3 2.8 73 Sufentanil 0.101 1 9.9 31.7% 18.5 6.7 74 Tapentadol 0.093 1 10.7 45.3% 28.0 6.5 75 Temazepam 0.106 1 9.4 17.7% 14.5 7.5 76 THC 1.566 1 0.6 57.0% 1.4 0.3 77 THC-COOH 0.305 1 3.3 45.8% 5.1 1.4 78 THC-OH 0.339 1 3.0 34.4% 6.2 1.6 79 Tramadol 0.100 1 10.0 40.7% 21.5 6.3 80 Triazolam 0.112 1 8.9 25.0% 15.7 6.8 81 Zaleplon 0.105 1 9.5 29.5% 16.6 6.7
The data was reviewed in Insight and Postrun exactly as described above in the linearity/reportable range section. Again, the first HiQC sample was used for each of the runs data. The first run was referred to as A and the second run was referred to as B. For runs A and B on days one through five a raw data table was created in Excel. These data tables included the residual sum of squares (RSS) values for each analyte from Postrun. The smaller the RSS value, the better the regression line fits the data. Postrun calculates this variance and represents this numerical information as the RSS value.
The number of calibrators was counted for each analyte for each run (A and B) for each of the five days. No more than four calibrators were dropped. No more than two of the lower end calibrators (0.40 ng/mL and 0.75 ng/mL) were dropped for one curve to avoid extrapolating the data because the lower calibrators were weighted more heavily. The only exception to this was the analyte Caffeine because an unknown factor was interfering with its’ signal and several times no peak was detected for the 0.40 ng/mL or the 0.75 ng/mL for both curves. As stated previously caffeine is irrelevant for forensics and was only included because of its addition to the shared stock solutions for the OF validation research project. All four data points were not detected so those calibrator points were not included in the analysis for those runs or days of data. Since the 50
calibration curves were run in duplicate there were a total of 16 calibrators instead of 8 for each
linear regression line.
The residual standard deviation was calculated from the RSS values. The residual
standard deviation formula is:
= 2 𝑅𝑅𝑅𝑅𝑅𝑅 𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆 � where Avg SD is the average residual standard deviation,𝑛𝑛 − RSS is the residual sum of squares, and
n is the number of calibrators (16 if no calibrators were dropped) and n-2 is the degrees of
freedom (Benoit, 2010).
The LOD was calculated. The formula that was used was:
3.3 = ∗ 𝑎𝑎𝑎𝑎𝑎𝑎 𝑆𝑆𝑆𝑆 𝐿𝐿𝐿𝐿𝐿𝐿 Where the 3.3 is the S/N ratio, avg SD is the average𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 standard deviation or the average residual
standard deviation and the slope could also be referred to as the 1st coefficient (ICH Q8, 2005;
SWGTOX, 2013). The equation of a line is y = ax+b where a is the 1st coefficient of the slope of
a line. The slope was copied from Postrun and pasted into Excel as well. This is just one way in
which the LOD can be determined. Other methods for determining LOD can include: a visual
evaluation, S/N, and the SD of the response and the slope (ICH Q8, 2005). The SD of the
response and slope also include either the evaluation of a blank or calibration curve (ICH Q8,
2005). A combination of the S/N ratio and standard deviation of the response and the slope using
a calibration curve was used as shown above.
The lower limit of quantitation (LLOQ) was calculated using the following formula:
10 = ∗ 𝑎𝑎𝑎𝑎𝑎𝑎 𝑆𝑆𝑆𝑆 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 51
Where the 10 is the S/N ratio and the other variables mean the same thing as mentioned above
for LOD (ICH Q8, 2005). The methods that are used to determine the LLOQ are the same as the
methods previously mentioned for determining the LOD (ICH Q8, 2005).
The data that was calculated for each of the runs (A and B) for each of the five days was
copied and pasted into the big summary LOD-LOQ Excel table. The LLOQ values were copied
over and an average of those values was calculated for each of the analytes. The cutoff
concentration was determined to be 1 ng/mL for all of the analytes. Next the cutoff of 1 ng/mL
was divided by the LLOQ values. This resulting value should be greater than or equal to the
cutoff value of 1.0 ng/mL. Ideally, the target ratio of the cutoff/LOQ should be greater than or
equal to 2.0. The CV% was then calculated using the following formula:
% =
𝑆𝑆𝑆𝑆𝑆𝑆 𝐶𝐶𝐶𝐶 Finally, the maximum and minimum ratios were𝐴𝐴𝐴𝐴𝐴𝐴 determined.𝐿𝐿𝐿𝐿𝐿𝐿 The maximum ratio was calculated
by dividing the cutoff by the minimum LLOQ value for each analyte, respectively. The
minimum ratio was calculated by dividing the cutoff by the maximum LLOQ value for each
analyte, respectively.
The only analyte that did not meet the criteria was THC because it had an average LOQ value of 1.566 ng/mL which is greater than the 1 ng/mL cutoff concentration. THC also had a cutoff/LOQ ratio that was less than 1.0. The cutoff for THC should be increased from 1 ng/mL to
2 ng/mL. The other problematic analytes, based on their minimum ratios were: butalbital, caffeine, cannabidiol, phenobarbital, THC, THC-COOH, and THC-OH.
4.3 Precision The precision results for the HiQC, LoQC, and SST were obtained from the five days of runs as well. The definition of precision is different and should not be confused with accuracy. 52
Precision is how close each data point is to one another for a specific set of samples. In this case, two sets of QCs were made fresh each day. For example, HiQC 1 and HiQC 2 were injected in duplicate and run twice within a 24-hour period. A total of 4 injections per batch, 8 injections per day, and 40 injections for the week. The concentrations for each of the QCs were copied from
Postrun and pasted into Excel. The average of concentrations was calculated. Using the calculated average concentration, the CV% was calculated for the batch, intra-day, inter-day, and week. The CV% was calculated according to the formula mentioned above but instead of using the average LOQ, the average of the precision data was used in the denominator. The batch CV% showed the variance between each batch within a 24-hour period. Two batch files were run each day. The intra-day shows the variation between the duplicate runs within the 24-hour time period. The inter-day shows the variation of the replicate runs between days. The week CV% shows the variation between day1 through day 5. This was the same for the HiQC and LoQC, samples. The highlighted CV% values were above the ideal 20%. 53
Table 3: HiQC Precision Results Intra- Inter- Avg Batch day day Week # Analyte - HiQC (25 ng/mL) (ng/mL) CV% CV% CV% CV% 10,11-Dihydro-10- 1 Hydroxycarbamazepine 35.5 7.9% 6.9% 6.6% 8.8% 2 2-Hydroxyethylflurazepam 27.0 8.7% 6.3% 6.2% 9.7% 3 4-Methylephedrine 34.5 6.6% 4.6% 4.6% 6.7% 4 6-MAC 25.5 8.2% 6.6% 6.3% 9.1% 5 6-MAM 26.9 6.4% 4.2% 4.2% 6.6% 6 7-Aminoclonazepam 26.6 10.8% 9.9% 9.8% 13.9% 7 Alfentanil 25.3 7.2% 5.3% 5.2% 7.3% 8 Alprazolam 28.7 6.8% 5.3% 5.1% 8.0% 9 Amo-Pentobarbital 25.8 8.6% 8.1% 7.2% 12.0% 10 Amphetamine 25.0 7.6% 5.5% 5.2% 8.1% 11 Benzoylecgonine 25.2 7.7% 6.3% 5.5% 9.3% 12 Buprenorphine 25.4 6.6% 4.5% 4.4% 7.0% 13 Bupropion 30.5 8.2% 6.9% 6.4% 9.2% 14 Butabarbital 26.2 7.4% 10.4% 6.1% 13.9% 15 Butalbital 29.4 11.1% 11.6% 8.9% 16.3% 16 Caffeine 29.0 26.7% 37.6% 26.0% 38.2% 17 Cannabidiol 32.5 18.5% 17.5% 16.7% 29.8% 18 Carisoprodol 24.4 6.4% 3.1% 2.9% 7.9% 19 Clonazepam 26.4 10.6% 8.8% 8.4% 12.1% 20 Cocaethylene 25.9 7.1% 5.2% 5.2% 7.2% 21 Cocaine 25.5 6.9% 5.1% 5.0% 7.1% 22 Codeine 26.8 5.7% 3.2% 2.9% 6.0% 23 Cotinine 24.5 8.1% 6.3% 6.0% 8.8% 24 Desalkylflurazepam 24.9 8.6% 6.2% 5.8% 11.5% 25 Desmethyltapentadol 25.2 7.8% 6.1% 6.0% 8.0% 26 Dextromethorphan 25.6 7.4% 5.6% 5.5% 7.5% 27 Dextrorphan 24.4 7.2% 5.3% 5.2% 7.4% 28 Diazepam 24.7 7.1% 5.7% 5.4% 8.0% 29 Dihydrocodeine 23.7 5.8% 3.5% 3.4% 6.1% 30 Ecgonine methyl ester 25.7 7.5% 5.2% 4.5% 9.2% 31 EDDP 24.4 7.2% 5.4% 5.4% 7.3% 32 Ephedrine 25.6 5.8% 4.1% 3.1% 8.1% 33 Fentanyl 24.4 7.6% 5.6% 5.5% 7.7% 34 Flurazepam 22.6 7.3% 5.3% 5.3% 7.4% 35 Gabapentin 25.5 5.8% 3.3% 2.7% 6.4% 36 Hydrocodone 26.2 5.5% 2.6% 2.5% 5.9% 37 Hydromorphone 26.9 5.7% 3.3% 3.2% 6.0% 38 Hydroxyalprazolam 25.4 7.0% 4.7% 4.1% 8.2% 54
39 Hydroxymidazolam 25.4 8.1% 6.1% 5.9% 8.7% 40 Hydroxytriazolam 25.2 7.9% 6.8% 6.0% 9.3% 41 Ketamine 25.2 7.3% 5.4% 5.4% 7.4% 42 Lorazepam 25.7 7.3% 5.4% 5.1% 8.1% 43 MDA 24.1 6.9% 4.9% 4.7% 7.4% 44 MDMA 24.2 6.7% 4.6% 4.6% 6.8% 45 MDPV 24.7 8.2% 7.0% 6.4% 9.3% 46 Meperidine 24.8 6.9% 4.7% 4.7% 7.0% 47 Meprobamate 24.2 6.8% 6.2% 5.7% 9.8% 48 Methadone 24.5 7.7% 6.0% 6.0% 7.8% 49 Methamphetamine 25.7 6.9% 4.8% 4.7% 7.0% 50 Methylone 24.6 6.5% 4.6% 4.5% 6.7% 51 Midazolam 25.7 7.9% 6.6% 6.2% 8.5% 52 Morphine 25.3 7.0% 4.7% 4.1% 8.1% 53 Nalbuphine 25.0 6.6% 5.0% 5.0% 6.9% 54 Norbuprenorphine 24.0 7.9% 6.5% 6.4% 8.6% 55 Norcodeine 25.3 6.4% 5.1% 4.9% 6.7% 56 Nordiazepam 26.7 9.3% 7.6% 7.3% 9.7% 57 Norfentanyl 25.9 7.8% 6.0% 6.0% 7.9% 58 Norhydrocodone 24.8 6.7% 4.4% 3.9% 7.4% 59 Norhydromorphone 24.0 6.6% 4.0% 3.7% 7.7% 60 Norketamine 25.0 7.5% 5.7% 5.7% 7.5% 61 Normeperidine 23.4 6.7% 5.0% 4.9% 6.9% 62 Noroxycodone 25.5 6.2% 4.4% 4.3% 6.5% 63 O-Desmethyl-cis-Tramadol 25.4 6.3% 4.1% 3.7% 8.1% 64 Oxazepam 25.6 9.2% 8.2% 8.0% 10.0% 65 Oxycodone 25.7 6.3% 4.3% 4.3% 6.4% 66 Oxymorphone 26.0 6.5% 4.3% 4.0% 7.6% 67 PCP 25.4 7.4% 5.6% 5.6% 7.6% 68 Pentazocine 23.8 7.1% 5.6% 5.4% 8.5% 69 Phenobarbital 25.0 8.5% 6.8% 5.9% 14.0% 70 Phentermine 28.3 6.6% 5.4% 4.9% 7.2% 71 Pregabalin 26.6 7.4% 5.7% 5.2% 8.7% 72 Secobarbital 24.8 5.8% 5.8% 5.1% 10.1% 73 Sufentanil 24.9 7.4% 5.8% 5.4% 7.9% 74 Tapentadol 24.3 6.8% 5.0% 5.0% 6.8% 75 Temazepam 26.9 10.1% 8.4% 8.2% 10.5% 76 THC 32.2 14.5% 12.2% 12.2% 14.7% 77 THC-COOH 30.7 9.6% 8.1% 6.7% 11.6% 78 THC-OH 27.3 7.9% 7.1% 6.9% 9.0% 79 Tramadol 24.7 7.3% 5.3% 5.3% 7.4% 80 Triazolam 25.2 7.0% 5.5% 5.2% 8.1% 81 Zaleplon 25.4 7.2% 5.5% 5.3% 7.5% 55
The concentration of the HiQC should have been 25 ng/mL for every analyte based on the HiQC_SS preparation guide in the appendix, with the exception of amobarbital and pentobarbital which were 12.5 ng/mL each, to make a combined concentration of 25 ng/mL.
Some of the analytes are over the ideal 25 ng/mL by a significant amount but that is more of a sample preparation accuracy problem rather than the instruments precision capability. As long as the HiQCs, that were being compared, were off by about the same amount during the sample preparation, then the accuracy of the sample preparation should not impact the precision significantly. However, if the HiQCs being compared are off by a significant amount then that would impact the precision based on the definition of precision. The CV% should be less than or equal to 20%. The only analyte that showed to be a constant problem was caffeine. The other problematic analyte was cannabidiol for the week CV%.
Table 4: HiQC Spread of the Precision Results with Caffeine
HiQC Batch CV% Intra-day CV% Inter-day CV% Week CV% Min 5.5% 2.6% 2.5% 5.9% Max 26.7% 37.6% 26.0% 38.2% Avg 7.9% 6.3% 5.8% 9.1%
Table 5: HiQC Spread of the Precision Results without Caffeine
HiQC Batch CV% Intra-day CV% Inter-day CV% Week CV% Min 5.5% 2.6% 2.5% 5.9% Max 18.5% 17.5% 16.7% 29.8% Avg 7.6% 5.9% 5.6% 8.7%
Table 4 shows the overall spread of the HiQC precision data, that includes caffeine, for the batch, intra-day, inter-day, and week CV%s. All of the maximum CV% values were above the 20% ideal value. The average values were calculated based on the CV% values in Table 3 for 56 the corresponding column headings. The most variation was observed over the week of runs.
Table 5 shows the overall spread of the HiQC precision data, that excludes caffeine. The maximum CV% values show that they were the most impacted when the caffeine CV% values were included in the data in Table 4 when compared to Table 5. The highlighted CV% values were above the ideal 20%. 57
Table 6: LoQC Precision Results Analyte - LoQC (60% CO) 0.6 Avg Batch Intra-day Inter-day Week # ng/mL (ng/mL) CV% CV% CV% CV% 10,11-Dihydro-10- 1 Hydroxycarbamazepine 0.90 8.8% 8.1% 7.7% 10.3% 2 2-Hydroxyethylflurazepam 0.60 9.1% 9.8% 8.0% 13.6% 3 4-Methylephedrine 0.87 7.6% 7.1% 6.2% 9.4% 4 6-MAC 1.18 8.0% 6.4% 6.0% 11.4% 5 6-MAM 0.59 9.9% 8.7% 8.5% 11.1% 6 7-Aminoclonazepam 0.63 16.1% 16.3% 15.0% 20.3% 7 Alfentanil 0.54 8.1% 6.5% 6.4% 8.5% 8 Alprazolam 0.62 7.4% 5.9% 5.8% 8.8% 9 Amo-Pentobarbital 1.04 17.1% 18.6% 10.8% 31.6% 10 Amphetamine 0.58 17.1% 17.8% 13.6% 24.2% 11 Benzoylecgonine 0.54 9.6% 7.4% 6.9% 11.4% 12 Buprenorphine 0.51 7.0% 5.1% 3.6% 11.9% 13 Bupropion 0.53 7.8% 6.4% 6.2% 9.8% 14 Butabarbital 0.57 18.7% 36.2% 12.4% 51.0% 15 Butalbital 0.44 48.1% 58.3% 43.8% 86.0% 16 Caffeine 0.15 156.3% 175.6% 114.8% 286.2% 17 Cannabidiol 0.58 34.7% 35.5% 22.7% 72.8% 18 Carisoprodol 1.10 8.7% 9.6% 8.1% 11.4% 19 Clonazepam 0.60 10.4% 10.5% 10.2% 12.8% 20 Cocaethylene 0.52 8.2% 6.7% 6.6% 8.5% 21 Cocaine 0.57 9.1% 7.6% 7.4% 9.6% 22 Codeine 0.54 10.8% 11.1% 10.2% 14.8% 23 Cotinine 0.56 16.7% 14.1% 13.6% 18.1% 24 Desalkylflurazepam 0.60 12.9% 12.2% 10.7% 16.9% 25 Desmethyltapentadol 0.54 8.2% 6.5% 6.4% 8.5% 26 Dextromethorphan 0.49 6.9% 5.5% 4.5% 8.2% 27 Dextrorphan 0.54 6.6% 5.9% 5.4% 7.9% 28 Diazepam 0.57 7.8% 6.7% 5.9% 9.2% 29 Dihydrocodeine 0.54 9.9% 8.3% 8.1% 11.1% 30 Ecgonine methyl ester 0.55 10.8% 10.6% 9.8% 12.9% 31 EDDP 1.05 6.5% 5.2% 5.1% 6.7% 32 Ephedrine 0.53 13.7% 12.1% 11.4% 15.8% 33 Fentanyl 0.51 5.6% 3.9% 3.7% 5.9% 34 Flurazepam 0.46 7.8% 6.3% 5.8% 8.7% 35 Gabapentin 0.49 17.5% 13.5% 12.0% 23.4% 36 Hydrocodone 0.49 12.8% 10.8% 10.7% 13.7% 37 Hydromorphone 0.57 10.5% 10.2% 10.0% 11.2% 38 Hydroxyalprazolam 0.49 10.4% 9.4% 9.2% 12.9% 39 Hydroxymidazolam 0.55 9.0% 7.6% 7.2% 10.8% 58
40 Hydroxytriazolam 0.51 9.1% 8.8% 7.9% 12.4% 41 Ketamine 0.51 6.8% 5.6% 5.4% 7.5% 42 Lorazepam 0.53 7.5% 7.1% 6.4% 10.1% 43 MDA 0.52 9.4% 8.9% 8.5% 12.2% 44 MDMA 0.53 8.5% 7.1% 7.0% 8.9% 45 MDPV 0.53 7.5% 6.0% 5.7% 9.6% 46 Meperidine 0.53 7.7% 6.3% 5.6% 9.1% 47 Meprobamate 1.02 8.8% 10.7% 7.8% 13.8% 48 Methadone 0.54 6.7% 4.9% 4.7% 6.9% 49 Methamphetamine 0.55 8.2% 7.4% 6.8% 10.9% 50 Methylone 0.48 8.9% 7.8% 7.7% 10.0% 51 Midazolam 0.59 6.5% 5.9% 4.9% 8.7% 52 Morphine 0.57 29.7% 29.6% 27.1% 36.5% 53 Nalbuphine 0.52 10.5% 9.3% 8.6% 13.3% 54 Norbuprenorphine 1.28 9.0% 7.7% 7.4% 12.8% 55 Norcodeine 0.51 15.5% 15.4% 13.2% 19.8% 56 Nordiazepam 0.58 5.5% 4.1% 3.5% 6.7% 57 Norfentanyl 1.08 7.2% 5.3% 5.2% 7.5% 58 Norhydrocodone 0.57 9.6% 8.4% 6.6% 14.8% 59 Norhydromorphone 0.57 8.9% 9.3% 7.5% 11.7% 60 Norketamine 0.55 5.8% 4.3% 4.2% 6.3% 61 Normeperidine 0.54 8.3% 6.9% 6.8% 8.9% 62 Noroxycodone 0.54 8.2% 6.7% 5.4% 10.8% 63 O-Desmethyl-cis-Tramadol 0.94 8.6% 7.6% 7.2% 10.7% 64 Oxazepam 0.53 7.7% 6.1% 5.2% 9.6% 65 Oxycodone 0.54 9.3% 8.1% 8.0% 10.4% 66 Oxymorphone 0.53 11.6% 12.2% 10.4% 14.9% 67 PCP 0.53 5.5% 3.6% 3.3% 6.4% 68 Pentazocine 0.51 8.5% 7.3% 6.8% 10.3% 69 Phenobarbital 0.33 59.2% 90.2% 45.9% 133.6% 70 Phentermine 0.56 11.1% 11.4% 9.2% 21.0% 71 Pregabalin 0.54 28.0% 28.4% 25.2% 36.9% 72 Secobarbital 0.50 15.5% 24.7% 13.0% 39.0% 73 Sufentanil 0.58 8.1% 6.9% 6.8% 8.7% 74 Tapentadol 0.52 7.8% 6.2% 6.0% 8.3% 75 Temazepam 0.55 12.6% 10.7% 10.4% 14.3% 76 THC 0.94 29.6% 26.0% 20.4% 41.7% 77 THC-COOH 0.56 19.6% 22.7% 17.0% 35.6% 78 THC-OH 0.50 16.7% 26.3% 5.0% 39.6% 79 Tramadol 0.51 7.2% 5.4% 5.2% 8.1% 80 Triazolam 0.55 7.6% 7.2% 6.3% 9.2% 81 Zaleplon 0.53 7.9% 6.5% 6.2% 9.6% 59
The concentration of the LoQC should have been 60% of the cutoff, which would have
been 0.6 ng/mL with the cutoff value being 1 ng/mL. The average concentrations of the LoQCs was off by a significant amount between analytes, which would have been a sample preparation accuracy issue. Since, the LoQC concentration is below the cutoff concentration it was to be expected that the CV% values would be higher, so the ideal CV% should have been lower than
40%. The highlighted CV% values were above the ideal 40%. There was a lot more variation for the LoQCs than there was for the HiQCs. The problematic analytes were butalbital, caffeine, and phenobarbital for the batch, intra-day, inter-day, and week CV% values, with caffeine having the highest CV% values. For the week CV% only, butabarbital and THC were problematic.
Table 7: LoQC Spread of the Precision Results with Caffeine
LoQC Batch CV% Intra-day CV% Inter-day CV% Week CV% Min 5.5% 3.6% 3.3% 5.9% Max 156.0% 175.6% 114.8% 286.2% Avg 13.8% 14.1% 10.8% 20.8%
Table 8: LoQC Spread of the Precision Results without Caffeine
LoQC Batch CV% Intra-day CV% Inter-day CV% Week CV% Min 5.5% 3.6% 3.3% 5.9% Max 59.2% 90.2% 45.9% 133.6% Avg 11.9% 12.0% 9.5% 17.4%
Table 7 shows the overall spread of the LoQC precision data that includes caffeine. The
maximum CV% values for the batch, intra-day, inter-day, and week were all significantly above
the 40% as indicated by the red highlighting. The average CV% values were close to or under the
ideal 20%. The most variation was observed for the week runs. Table 8 shows the overall spread
of the LoQC precision data that excludes caffeine. Again, the maximum CV% values showed to
be the most impacted by caffeine when Table 7 is compared to Table 8. By excluding the 60 caffeine data in the overall average of the week CV% values the previous 20.8% which was above the ideal 20% was brought down to 17.4% which is now below the ideal 20%. 61
Table 9: SST Precision Results Avg Intra-day Inter-day Week # Analyte - SST (Cutoff) 1 ng/mL (ng/mL) CV% CV% CV% 10,11-Dihydro-10- 1 Hydroxycarbamazepine 0.92 10.8% 10.0% 11.6% 2 2-Hydroxyethylflurazepam 0.94 9.9% 9.5% 10.7% 3 4-Methylephedrine 0.94 9.8% 9.5% 10.3% 4 6-MAC 0.89 12.5% 11.5% 14.9% 5 6-MAM 0.89 9.3% 9.1% 9.4% 6 7-Aminoclonazepam 0.95 14.9% 14.4% 17.7% 7 Alfentanil 0.92 7.9% 7.7% 8.1% 8 Alprazolam 0.94 8.7% 8.5% 9.1% 9 Amo-Pentobarbital 0.71 28.4% 25.5% 36.0% 10 Amphetamine 0.91 10.2% 8.3% 12.2% 11 Benzoylecgonine 0.90 11.6% 11.0% 12.0% 12 Buprenorphine 0.96 10.7% 10.5% 12.5% 13 Bupropion 0.89 7.9% 7.5% 9.0% 14 Butabarbital 0.94 39.5% 29.3% 44.2% 15 Butalbital 0.69 28.1% 22.4% 38.7% 16 Caffeine 0.43 247.8% 135.5% 317.4% 17 Cannabidiol 0.84 35.1% 28.5% 46.8% 18 Carisoprodol 0.89 5.4% 3.4% 7.1% 19 Clonazepam 1.03 10.9% 10.5% 11.6% 20 Cocaethylene 0.91 8.1% 7.9% 8.2% 21 Cocaine 0.92 9.7% 9.6% 9.8% 22 Codeine 0.92 10.9% 9.8% 11.8% 23 Cotinine 0.93 11.0% 10.2% 11.6% 24 Desalkylflurazepam 0.97 9.9% 9.0% 12.1% 25 Desmethyltapentadol 0.92 7.2% 7.0% 7.5% 26 Dextromethorphan 0.92 6.8% 6.6% 7.1% 27 Dextrorphan 0.91 7.2% 6.9% 7.4% 28 Diazepam 0.93 9.4% 8.9% 10.1% 29 Dihydrocodeine 0.91 7.6% 7.4% 7.9% 30 Ecgonine methyl ester 0.88 11.2% 10.9% 13.9% 31 EDDP 0.93 7.3% 7.2% 7.4% 32 Ephedrine 0.91 10.5% 9.9% 12.0% 33 Fentanyl 0.92 7.8% 7.8% 7.8% 34 Flurazepam 0.92 8.0% 7.9% 8.0% 35 Gabapentin 0.90 7.5% 4.0% 11.0% 36 Hydrocodone 0.88 11.6% 11.6% 12.0% 37 Hydromorphone 0.90 12.0% 11.8% 12.3% 38 Hydroxyalprazolam 0.89 8.1% 7.2% 9.5% 39 Hydroxymidazolam 0.92 6.3% 5.7% 7.5% 62
40 Hydroxytriazolam 0.90 8.1% 7.7% 9.1% 41 Ketamine 0.93 8.6% 8.4% 8.6% 42 Lorazepam 0.93 9.4% 8.0% 10.4% 43 MDA 0.91 8.9% 8.0% 11.0% 44 MDMA 0.92 8.6% 8.5% 8.8% 45 MDPV 0.90 10.0% 9.7% 10.8% 46 Meperidine 0.92 9.1% 8.8% 9.3% 47 Meprobamate 0.88 9.1% 7.6% 10.9% 48 Methadone 0.91 8.2% 8.2% 8.3% 49 Methamphetamine 0.92 9.8% 9.4% 10.4% 50 Methylone 0.93 10.3% 10.3% 10.5% 51 Midazolam 0.93 7.0% 6.8% 7.6% 52 Morphine 0.92 10.5% 8.0% 15.4% 53 Nalbuphine 0.91 8.0% 7.9% 9.1% 54 Norbuprenorphine 0.93 9.2% 8.1% 12.8% 55 Norcodeine 0.91 11.7% 8.2% 12.7% 56 Nordiazepam 0.94 5.5% 5.3% 6.2% 57 Norfentanyl 0.91 7.3% 7.1% 7.4% 58 Norhydrocodone 0.88 12.8% 12.6% 14.0% 59 Norhydromorphone 0.88 10.2% 9.7% 12.5% 60 Norketamine 0.93 6.8% 6.6% 7.0% 61 Normeperidine 0.91 8.2% 8.1% 8.2% 62 Noroxycodone 0.89 11.3% 10.9% 11.7% 63 O-Desmethyl-cis-Tramadol 0.91 9.3% 8.8% 11.1% 64 Oxazepam 0.96 7.0% 5.6% 8.1% 65 Oxycodone 0.93 9.0% 8.7% 9.4% 66 Oxymorphone 0.89 11.2% 9.7% 13.6% 67 PCP 0.92 7.0% 6.9% 7.3% 68 Pentazocine 0.91 8.4% 8.3% 9.5% 69 Phenobarbital 0.67 48.7% 37.2% 72.4% 70 Phentermine 0.90 13.9% 11.0% 16.8% 71 Pregabalin 0.98 9.4% 5.7% 14.4% 72 Secobarbital 0.84 21.1% 17.3% 29.4% 73 Sufentanil 0.92 6.8% 6.8% 7.1% 74 Tapentadol 0.91 7.9% 7.8% 8.1% 75 Temazepam 1.00 12.1% 11.7% 12.6% 76 THC 1.04 31.3% 27.0% 36.9% 77 THC-COOH 0.98 20.2% 18.2% 24.2% 78 THC-OH 0.91 18.0% 14.4% 22.4% 79 Tramadol 0.92 6.4% 6.3% 6.7% 80 Triazolam 0.95 8.4% 8.3% 9.7% 81 Zaleplon 0.94 7.8% 7.0% 8.4% 63
The system suitability test is treated as an unknown QC sample. The SST concentration
should have been 1 ng/mL just like the cutoff. The SST sample preparation was different from
the HiQC and LoQC samples because instead of preparing two SST samples only one was
prepared. A total of 2 injections per batch, 4 injections per day, and 20 injections for the week.
Most of the average SST concentrations were close to 1 ng/mL. The ideal CV% should have
been less than 20%. The CV% values that were greater than the ideal 20% are highlighted in red.
More variation was observed for more analytes with the SST precision than the HiQC precision.
The analytes that had CV% values above the ideal 20% were as follows: amo-pentobarbital,
butabarbital, butalbital, caffeine, cannabidiol, phenobarbital, secobarbital, THC, THC-COOH,
and THC-OH. Caffeine had the highest observed CV% values.
Table 10: SST Spread of the Precision Results with Caffeine
SST Intra-day CV% Inter-day CV% Week CV% Min 5.4% 3.4% 6.2% Max 247.8% 135.5% 317.4% Avg 14.4% 11.9% 17.2%
Table 11: SST Spread of the Precision Results without Caffeine
SST Intra-day CV% Inter-day CV% Week CV% Min 5.4% 3.4% 6.2% Max 48.7% 37.2% 72.4% Avg 11.5% 10.3% 13.5%
Table 10 shows the overall spread of the SST precision data across the panel of all of the
analytes including caffeine. The maximum CV% values exceeded the ideal 20% by a significant
amount for the intra-day, inter-day, and week as indicated by the red highlighting. However, the average CV% values were all under the ideal 20%. Table 11 shows the spread of the SST precision data excluding caffeine. Since, all of the overall average CV% values were already 64 under the ideal 20% by excluding the caffeine it did not have much impact other than further lowering the CV% values. However, by excluding the caffeine data it did lower the maximum
CV% values significantly when Table 10 is compared to Table 11. 65
CHAPTER 5. DISCUSSION 5.1 Linearity/Reportable Range The R2 value is the ratio of the variance of the estimated regression line equation over the total amount of variance (Sweeney, 2020). The total amount of variance includes the unexplained error associated with the residual sum of squares and the variation associated with the regression line equation (Sweeney, 2020). The R2 will be directly impacted by how close the data points are to the regression line. If the concentrations for the same calibrator or QC sample differ significantly from the regression line, then the variance or error associated with the sum of squares will be large and the ratio will be farther away from 1 and vice versa. This will occur because the error associated with the sum of squares is located in the denominator of the ratio.
The R2 values will range from 0 to 1. The closer the ratio is to 1 the better the estimated regression line is fit to the data set (Sweeney, 2020). This means that there is a relationship between the independent and dependent variables. The opposite is true with an R2 vale of 0.
For example, the R2 values of 0.528 for butalbital and 0.048 for caffeine indicated that there was no correlation between the dependent and the independent variables. These R2 values of 0.528 and 0.048 also showed that the estimated regression line did not fit the data. This is why those data points were dropped from the calculations in Table 1. They were considered outliers in comparison to their other runs and when compared to the other analytes within the panel. For
6-MAM and normeperidine they both had a maximum R2 value of 1.0 (when rounded in Excel), which indicates a correlation between the dependent and independent variables. Also, the estimated regression line was a good fit to the data for those analytes. 66
5.2 Limit of Detection
The RSS value measures the unexplained errors that exists in a particular data set
(Sweeney, 2020). As stated by Sweeney (2020), “The difference between the observed value of y
and the value of y predicted by the estimated regression equation is called a residual”. This is
done for each data point within a particular data set. These residuals are then summed and
squared to act as a variance measurement. In general statistics, the variance is calculated by
adding all of the data points together and then dividing by the total number of points to get an
average for the overall data. Next, the points are subtracted from the average and then squared.
Then to calculate the SD, the square root of the variance is taken. Essentially, the computer is
calculating a more scientific rigorous variance when the program, Postrun, reports the RSS
values. The smaller the RSS value, the better the regression line fits the data.
The regression line shows the relationship between a set of variables. These variables are
defined as dependent and independent (Sweeney, 2020; Benoit, 2010). The results obtained from
a specific data set can provide an estimate for future analysis and can create a best fit line. The
equation that results from the best fit line, determined by a set of known calibrators, can then be
used to determine where future or unknown concentrations would fall on that best fit regression
line. From the estimated regression line y = ax+b, the 1st coefficient (a), or slope can be obtained.
Because one calibration curve was run in duplicate this provided a total number of 16 calibrators rather than 8. By running the calibration curve in duplicate, this allowed more points to be dropped if the chromatography was bad or if peaks were not detected. If only one calibration curve was run only 2 points could have been dropped, whereas running in duplicate, 4 points could be dropped. By running in duplicate this will help with the standard deviation calculation. This is due to the degrees of freedom. 67
In this analysis, the degrees of freedom (DOF) were n-2. By having a minimum of 12
data points vs 6 data points (if 4 points were dropped from 16 or if 2 data points were dropped
from 8), the resulting DOF would be 10 and 4, respectively. The higher the ‘n’ in the DOF
equation (n-2), the better the standard deviation. This is because in the average standard
deviation equation the DOF equation is in the denominator so there is an inverse relationship. As
the ‘n’ value in the (n-2) equation gets larger, so more data points, the lower the standard
deviation and vice versa. Normally, the degrees of freedom are n-1 but that will only provide an
estimate of the SD for an overall population of data. For example, if a data set contains a
population of 10 measurements but the 10th measurement is unknown but is included in the overall average for the data set the 10th unknown measurement can be back calculated. N-2 is
used because included in the RSS is the two parameters of the regression line y= ax+b, the slope
(a) and the y-intercept (b) (Benoit, 2010). The n-2 can account for the estimate of a SD on a
sample in a population versus the entire population as with n-1. In general statistics, the SD is
obtained by taking the square root of the variance. For this project, essentially the same thing
was being done. The SD was calculated by taking the square root of the RSS, which acted as the
variance, and to account for the number of parameters included in the RSS, the RSS was divided
by the DOF (Benoit, 2010).
As noted in the results section, the weighting for THC was changed from the default of
1/C2 to 1/C. As mentioned above the estimated regression line is based on calibrators. The higher
calibrators will experience a higher variance and will therefore, influence the amount of error
associated with the lower calibrators if the line is not weighted (Dolan, 2018). In order to account
for this, the curve data should be weighted inversely to their concentrations (Dolan, 2018). By
weighting the data, it should decrease the error or variance across the curve and the method 68
should produce better results (Dolan, 2018). A weighting of 1/C2 is preferred but was not
feasible for THC. Consistently the R2 values for THC were below 0.950 with the 1/C2 weighting.
In order to improve the R2 values the weighting was changed to 1/C. This also noticeably
changed the LOQ for THC. When the curves are not weighted, as in the case of THC, the LOQ
will be increased (Dolan, 2018). This was observed in the data. The cutoff for THC showed that
1 ng/mL would not be able to quantitate the analyte. Based on the data it is recommended that
the cutoff for THC be increased to 2 ng/mL. The reason for changing the weighting of THC and
for also increasing the cutoff for THC was because THC was experiencing absorptive losses. As
mentioned previously in the background, it is known that THC is lipophilic and likes to “stick”
to fat or adipose tissue; however, THC also likes to stick to plastic. This was tested by preparing
a vial of THC in glass filter vials and plastic filter vials. It was originally thought that the glass
filter vials would decrease the absorptive loss of THC, but this was not the case entirely. While
the plastic filter vials showed to have less absorptive losses for THC, the plastic filter vials did
still show signs of absorptive losses for THC. Again, this experiment was done using OF, but the
same results were reflected in blood. During the blood sample preparation, the analytes are
mostly in glass vials, but the HybridSPE-Phospholipid cartridges are plastic and absorptive
losses for THC could be experienced. This is also why further research needs to be conducted on
the percent recovery of the analytes from the SPE process. The main reason why the weighting
for THC had to be changed from the default of 1/C2 to 1/C was because the absorptive losses
were mainly affecting the lower calibrators the most such as the 0.40 ng/mL, 0.75 ng/mL and
CO. As mentioned previously, with a calibration curve that is weighted with 1/C2 the lower calibrators are heavily weighted. This created problems with THC such as the lower R2 values 69
and the increase in the cutoff value. By changing the weighting of THC to 1/C more weight was
assigned or distributed to the higher calibrators that were less affected by the absorptive losses.
5.3 Precision
The LoQC_SS should have been remade since the analyte concentrations were not accurate when compared to the expected 0.6 ng/mL. In Insight, on the regression line, boxes are shown which indicate how close the calibrators and QCs are to each other or how precise they
are, and how close they are to the regression line which is how accurate they are. If they are far
from the line and spread out from one another they are said to be neither accurate nor precise. If
they are grouped together but still off the regression line, they are precise but not accurate. If
some of them are on the line but not grouped closely together then some are accurate and still not
precise. If they are grouped tightly and are on the regression line, then they are precise and
accurate. Ideally, the calibrators and the QCs should be both precise and accurate in order to
estimate the concentration of an analyte in a patient or unknown sample from the estimated
regression line equation. The analytes that were not precise would have impacted the precision
results for the validation study.
The stock solutions that were made for the CCSS, HiQC_SS, LoQC_SS, and ISTD_SS
could have also experienced errors during the preparation step. There are two types of errors,
systematic and random. Systematic errors usually affect the accuracy and random errors usually
affect the precision. Systematic errors are usually related to the instruments or equipment used in
making measurements and can be corrected (Harris, 2010, p.50). The sample preparation would
be classified as a systematic error. It was noticed during the pipetting stage that when the 10-100
µL pipet was used with the 1-200 µL tips, the tips would fall off when delivering the analyte
from the vial to the volumetric flask. This happened for multiple analytes introducing systematic 70 errors which is why the analyte concentration was off from the expected analyte concentration.
This systematic error could have been corrected by making a new stock solution with a different pipet. Random errors are considered unknown and unpredictable (Harris, 2010, p.51). Random errors could also result from the instruments that are making the measurements (Harris, 2010, p.51). Random errors could have resulted from the ESI source becoming ‘dirty’ from the artificial serum matrix. This random error could have negatively impacted the precision data.
This random error is decreased but it is not completely eliminated with the use of an abbreviated
2-hour ACN bakeouts to clean the ESI source.
5.4 Overall Analysis
Based on the above results, the problematic analytes were consistent across all of the validation parameters. The 10 problematic analytes consisted of cannabinoids and barbiturates and contributed to the ~12% that failed at least one of the validation parameters. Barbiturates are acidic drugs and will bind to albumin within serum (Goldbaum, 1954). This could have attributed to the decrease in signal for the barbiturates because the artificial serum contained albumin. This can also partially account for the barbiturates overall poor performance. Caffeine exhibited the highest CV% values and in some cases could have acted as an outlier when compared to the other CV% values for the other analytes, causing the average overall CV% values to be elevated. Therefore, the second set of overall spread of the precision tables that was included in the results section was to show the impact that the elevated CV% values for caffeine had on the rest of the overall data. However, 71 out of the 81 or ~87% of the analytes passed all of the validation parameters that were tested.
Caffeine is not an illicit drug, and neither are some of the other analytes listed in the drug panel. The other non-illicit drugs were detected and quantified because they are also found in 71
“street” drug samples as cutting agents. These cutting agents usually look visually similar and induce the same effects as the illicit drug. That is why other non-illicit drugs were quantified in this research project. As far as caffeine, this research project was done in conjunction with another project using the same stock solutions. The other project consisted of validating the same
LC-MS/MS instrument but with the use of OF instead of blood. The caffeine was added specifically for the OF project as an internal tracer to back calculate the amount of human saliva that was collected using the oral wash collection kit. As far as blood, caffeine really does not have a purpose as far as an illicit drug. This is also another reason why caffeine was excluded in the second set of precision tables in the results section. While caffeine’s poor performance was observed it did not have an overall impact on the validation of the method.
A few possibilities as to why the CV% values were so high for caffeine was based on the chromatography of the peaks in Insight. The retention time for caffeine should have been around
2.567 based on the standard analyte instrument parameters found in the appendix that was obtained from LabSolutions. When analyzing the data in Insight, the computer program would often integrate the peak that was next to where caffeine should have eluted. By clicking on the peak that was often misidentified as caffeine, in Postrun it was found to be segment number 41.
Caffeine has a segment number of 43. This segment number refers to the event number. The event number of 41 was then found, in the standard analyte instrumental parameters in the appendix, to match nor-fentanyl which has a retention time of 2.526. Without reviewing the data in Insight first, there could have been false positives for caffeine when the computer integrated the nor-fentanyl peak instead of indicating that no peak was identified for caffeine.
One reason caffeine may not have had a peak present is that it could have been experiencing ion suppression due to an interferent in the matrix or other source. For most of the 72
LoQC and SST samples a peak was often not detected for the lower calibrators such as the 0.40 ng/mL and 0.75 ng/mL, which explains why the LoQC at 0.6 ng/mL had such a high CV% value for caffeine. Without the identification of a peak, that could have indicated that the concentration of the caffeine was below the LOD and LOQ. This could have been attributed to loss of the analyte due to the multiple times in which the sample changed amber vials, multiple pipet steps, the precipitation of proteins, SPE process, evaporation of the ACN, and reconstitution in the test tubes. All of these could have resulted in a loss of the analytes, not just caffeine.
Also, it was observed that the artificial serum is not fully cleaned up with the precipitating agent and the SPE process. The detector lost signal fairly quickly when multiple runs were run back to back. That is why the abbreviated 2-hour ACN bakeouts were implemented in between each day. Ideally a bakeout should have been done after each run but given time constraints between the runs it was not feasible for this study. The analytes that were the first to reflect the loss of signal were the barbiturates, cannabidiol, THC-COOH, and THC-
OH, which were all run in negative mode. They are also the same analytes that were considered problematic across the validation parameters. 73
CHAPTER 6. CONCLUSION In conclusion, the main goal of this research was to validate a method to detect and
quantify illicit drug use in blood utilizing SPE and LC/MS/MS following the guidelines outlined
in the Clinical Lab Consulting Method Validation Protocol. The results showed that the LC-MS/
MS was capable of detecting and quantifying illicit drug use in artificial serum using the
guidelines that were outlined in the Clinical Lab Consulting Method Validation Protocol. The
overall objective of this research was to evaluate multiple parameters such as: linearity or the reportable range, limit of detection (LOD) or analytical sensitivity, and precision for the analysis
of illicit drug use in blood. These parameters were successfully evaluated with a few problematic
analytes that were discussed in the discussion section. The specific aim of this research was to
validate SPE coupled with LC/MS/MS methods for the detection and quantitation of illicit drug
use in blood utilizing the guidelines from Clinical Lab Consulting Method Validation Protocol.
However, the following validation parameters were not tested: analytical accuracy,
patient correlation with human blood samples, carryover, interference, and matrix effects. The
above validation parameters were not tested due to multiple unforeseen circumstances. These
circumstances included altering the sample preparation methods to obtain better peak shapes,
signals, and overall analyte sensitivity. Also, the nitrogen generator malfunctioned causing down
time until liquid nitrogen tanks were ordered which created a new problem. The gas from the
liquid nitrogen tanks was not sufficient to run the LC-MS/MS at times resulting in the
instrument shutting down in the middle of multiple runs. In those cases, the data could not be
used and a whole new calibration curve was prepared. This happened multiple times which caused further delays until the new nitrogen generator had arrived. Due to unforeseen circumstances with the quick widespread pandemic of COVID-19 the lab access on the campus was denied to 74 researchers for safety reasons. The specific aim will be met once all of the validation parameters are successfully tested.
Further research should include testing the above-mentioned validation parameters including analytical accuracy, patient correlation using human blood samples, carryover, interference, and matrix effects. The percent recovery of all the analytes should be tested to quantify the amount of each analyte that is lost due to the SPE process. This will help to account for losses in the analysis and also determine if spike volumes should be increased to account for the losses. It is also recommended that the precision data be repeated for the LoQC with a fresh
LoQC_SS, using a different pipet. These parameters can further determine the validity of the LC-
MS/MS instrument as well as the method for determining the presence and quantity of illicit drugs within a sample. 75
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APPENDIX A. STANDARD ANALYTES
Cer# Analyte Concentration (µg/mL) Mode Cer# Analyte Concentration (µg/mL) Mode D-091 10,11-Dihydro-10-Hydroxycarbamazepine 1,000 Positive M-013 MDMA 1,000 Positive M-158 4-Methylephedrine 1,000 Positive M-146 MDVP 1,000 Positive F-902 2-Hydroxyethylflurazepam 1,000 Positive M-035 Meperidine 1,000 Positive A-053 6-MAC 1,000 Positive M-039 Meprobamate 1,000 Positive A-009 6-MAM 1,000 Positive M-007 Methadone 1,000 Positive A-916 7-Aminoclonazepam 1,000 Positive M-009 Methamphetamine 1,000 Positive A-071 Alfentanil 1,000 Positive M-140 Methylone 1,000 Positive A-903 Alprazolam 1,000 Positive M-908 Midazolam 1,000 Positive A-020 Amobarbital 1,000 Negative M-005 Morphine 1,000 Positive A-007 Amphetamine 1,000 Positive N-051 Nalbuphine 1,000 Positive B-004 Benzoylecgonine 1,000 Positive N-059 Norbuprenorphine 1,000 Positive B-044 Buprenorphine 1,000 Positive N-005 Norcodeine 1,000 Positive B-034 Bupropion 1,000 Positive N-905 Nordiazepam 1,000 Positive B-024 Butabarbital 1,000 Negative N-031 Norfentanyl 1,000 Positive B-006 Butalbital 1,000 Negative N-053 Norhydrocodone 1,000 Positive C-051 Caffeine 1,000 Positive N-107 Norhydromorphone 1,000 Positive C-045 Cannabidiol 1,000 Negative N-036 Norketamine 1,000 Positive C-077 Carisoprodol 1,000 Positive N-089 Normeperidine 1,000 Positive C-907 Clonazepam 1,000 Positive N-011 Noroxycodone 1,000 Positive C-010 Cocaethylene 1,000 Positive T-035 O-Desmethyl-cis-tramadol 1,000 Positive C-008 Cocaine 1,000 Positive O-902 Oxazepam 1,000 Positive C-006 Codeine 1,000 Positive O-002 Oxycodone 1,000 Positive C-016 Cotinine 1,000 Positive O-004 Oxymorphone 1,000 Positive D-915 Desalkylflurazepam 1,000 Positive P-007 PCP 1,000 Positive D-052 Desmethyltapentadol 1,000 Positive P-073 Pentazocine 1,000 Positive D-907 Diazepam 1,000 Positive P-010 Pentobarbital 1,000 Negative D-019 Dihydrocodeine 1,000 Positive P-008 Phenobarbital 1,000 Negative E-001 Ecgonine methyl ester 1,000 Positive P-023 Phentermine 1,000 Positive E-022 EDDP 1,000 Positive P-066 Pregabalin 1,000 Positive E-011 Ephedrine 1,000 Positive S-002 Secobarbital 1,000 Negative F-013 Fentanyl 1,000 Positive S-008 Sufentanil 100 Positive F-003 Flurazepam 1,000 Positive T-058 Tapentadol 1,000 Positive G-007 Gabapentin 1,000 Positive T-907 Temazepam 1,000 Positive H-003 Hydrocodone 1,000 Positive T-005 THC 1,000 Positive H-004 Hydromorphone 1,000 Positive T-019 THC-COOH 1,000 Negative A-907 Hydroxyalprazolam 1,000 Positive H-027 THC-OH 1,000 Negative H-922 Hydroxymidazolam 1,000 Positive T-027 Tramadol 1,000 Positive T-911 Hydroxytriazolam 1,000 Positive T-910 Triazolam 1,000 Positive K-002 Ketamine 1,000 Positive Z-004 Zaleplon 1,000 Positive L-901 Lorazepam 1,000 Positive D-013 Dextromethorphan 1,000 Positive M-012 MDA 1,000 Positive D-034 Dextrorphan 1,000 Positive 81
APPENDIX B. INTERNAL STANDARDS
Cer# Internal Standard Concentration (µg/mL) Mode A-006 6-MAM-D3 100 Positive A-005 Amphetamine-D5 100 Positive B-001 Benzoylecgonine-D3 100 Positive B-901 Buprenorphine-D4 100 Positive B-065 Butabarbital-D5 100 Negative C-084 Cannabidiol-D3 100 Negative C-009 Cocaethylene-D3 100 Positive C-004 Cocaine-D3 100 Positive C-005 Codeine-D3 100 Positive C-017 Cotinine-D3 100 Positive D-902 Diazepam-D5 100 Positive E-021 EDDP-D3 100 Positive F-001 Fentanyl-D5 100 Positive H-047 Hydrocodone-D6 100 Positive H-049 Hydromorphone-D6 100 Positive K-003 Ketamine-D4 100 Positive L-902 Lorazepam-D4 100 Positive M-036 Meperidine-D4 100 Positive M-008 Methadone-D3 100 Positive M-004 Methamphetamine-D5 100 Positive M-918 Midazolam-D4 100 Positive M-003 Morphine-D3 100 Positive N-920 Norbuprenorphine-D3 100 Positive N-082 Norcodeine-D3 1,000 Positive N-903 Nordiazepam-D5 100 Positive N-054 Norhydrocodone-D3 100 Positive N-037 Norketamine-D4 100 Positive N-020 Normeperidine-D4 100 Positive N-032 Noroxycodone-D3 100 Positive O-901 Oxazepam-D5 100 Positive O-007 Oxycodone-D6 100 Positive P-003 PCP-D5 100 Positive P-009 Pentobarbital-D5 100 Negative P-018 Phenobarbital-D5 100 Negative T-059 Tapentadol-D3 100 Positive T-007 THCCOOH-D9 100 Negative Z-010 Zaleplon-D4 100 Positive T-011 THC-D3 1,000 Positive H-041 THC-OH-D3 100 Negative 82
APPENDIX C. CALIBRATION CURVE STOCK SOLUTION PREP GUIDE
Calibration Curve Stock Solution (CCSS) Prep Guide - Expiration = 1 year (Freezer) Analyte Stock Concentration (µg/mL) Spike Volume (µL) CCSS = 5,000 CO Cutoff (ng/mL) Initial Stock Conc. µg/mL Comments 10,11-Dihydro-10-Hydroxycarbamazepine 1000 50 5000 1 1000 4-Methylephedrine 1000 50 5000 1 1000 2-Hydroxyethylflurazepam 1000 50 5000 1 1000 6-MAC 1000 50 5000 1 1000 6-MAM 1000 50 5000 1 1000 7-Aminoclonazepam 1000 50 5000 1 1000 Refrigerator Alfentanil 1000 50 5000 1 1000 Alprazolam 1000 50 5000 1 1000 Amobarbital* 1000 25 2500 0.5 1000 Amphetamine 1000 50 5000 1 1000 Benzoylecgonine 1000 50 5000 1 1000 Buprenorphine 1000 50 5000 1 1000 Bupropion 1000 50 5000 1 1000 Butabarbital 1000 50 5000 1 1000 Butalbital 1000 50 5000 1 1000 Caffeine 1000 50 5000 1 1000 Cannabidiol 1000 50 5000 1 1000 Carisoprodol 1000 50 5000 1 1000 Clonazepam 1000 50 5000 1 1000 Cocaethylene 1000 50 5000 1 1000 Cocaine 1000 50 5000 1 1000 Codeine 1000 50 5000 1 1000 Cotinine 1000 50 5000 1 1000 Desalkylflurazepam 1000 50 5000 1 1000 Desmethyltapentadol 1000 50 5000 1 1000 Diazepam 1000 50 5000 1 1000 Ecgonine methyl ester 1000 50 5000 1 1000 EDDP 1000 50 5000 1 1000 Ephedrine 1000 50 5000 1 1000 Fentanyl 1000 50 5000 1 1000 Flurazepam 1000 50 5000 1 1000 Gabapentin 1000 50 5000 1 1000 Hydrocodone 1000 50 5000 1 1000 Hydromorphone 1000 50 5000 1 1000 Hydroxyalprazolam 1000 50 5000 1 1000 Hydroxymidazolam 1000 50 5000 1 1000 Hydroxytriazolam 1000 50 5000 1 1000 Ketamine 1000 50 5000 1 1000 Lorazepam 1000 50 5000 1 1000 MDA 1000 50 5000 1 1000 MDMA 1000 50 5000 1 1000 MDVP 1000 50 5000 1 1000 Meperidine 1000 50 5000 1 1000 Meprobamate 1000 50 5000 1 1000 Methadone 1000 50 5000 1 1000 Methamphetamine 1000 50 5000 1 1000 Methylone 1000 50 5000 1 1000 Midazolam 1000 50 5000 1 1000 Morphine 1000 50 5000 1 1000 Nalbuphine 1000 50 5000 1 1000 Norbuprenorphine 1000 50 5000 1 1000 Norcodeine 1000 50 5000 1 1000 Nordiazepam 1000 50 5000 1 1000 Norfentanyl 1000 50 5000 1 1000 Norhydrocodone 1000 50 5000 1 1000 Norhydromorphone 1000 50 5000 1 1000 Norketamine 1000 50 5000 1 1000 Normeperidine 1000 50 5000 1 1000 Noroxycodone 1000 50 5000 1 1000 O-Desmethyl-cis-tramadol 1000 50 5000 1 1000 Oxazepam 1000 50 5000 1 1000 Oxycodone 1000 50 5000 1 1000 Oxymorphone 1000 50 5000 1 1000 PCP 1000 50 5000 1 1000 Pentazocine 1000 50 5000 1 1000 Pentobarbital* 1000 25 2500 0.5 1000 Phenobarbital 1000 50 5000 1 1000 Phentermine 1000 50 5000 1 1000 Pregabalin 1000 50 5000 1 1000 Secobarbital 1000 50 5000 1 1000 Sufentanil 100 500 5000 1 100 100 mg/ml Tapentadol 1000 50 5000 1 1000 Temazepam 1000 50 5000 1 1000 THC 1000 50 5000 1 1000 THC-COOH 1000 50 5000 1 1000 THC-OH 1000 50 5000 1 1000 Tramadol 1000 50 5000 1 1000 Triazolam 1000 50 5000 1 1000 Zaleplon 1000 50 5000 1 1000 Dextromethorphan 1000 50 5000 1 1000 Dextrorphan 1000 50 5000 1 1000 Amount of the above analytes (µL) 4500 Amount of Methanol to Add (µL) 5500 Total Volume 10000 83
*Note: Amobarbital and Pentobarbital are isomers and coelute. So individual cutoffs were combined to give 100 ng/mL for Amo-Pentobarbital mix.
Total or final volume is brought up to 10 ml in volumetric flask with methanol 84
APPENDIX D. HIGH QC STOCK SOLUTION PREP GUIDE
High QC Stock Solution (HiQC_SS) Preparation - Expiration = 1yr (Freezer) Analyte Stock Concentration (µg/mL) Spike Volume (µL) HiQC SS = 25 ULLOQ ULLOQ (ng/mL) Initial Stock Conc. µg/mL Comments Amphetamine 1000 25 2500 100 1000 6-MAM 1000 25 2500 100 1000 Amobarbital* 1000 12.5 2500 100 1000 *Amo-Pento 6-MAC 1000 25 2500 100 1000 Alfentanil 1000 25 2500 100 1000 Alprazolam 1000 25 2500 100 1000 Hydroxyalprazolam 1000 25 2500 100 1000 7-Aminoclonazepam 1000 25 2500 100 1000 Refrigerator Benzoylecgonine 1000 25 2500 100 1000 Butalbital 1000 25 2500 100 1000 Butabarbital 1000 25 2500 100 1000 Bupropion 1000 25 2500 100 1000 Buprenorphine 1000 25 2500 100 1000 1 mg/ml Codeine 1000 25 2500 100 1000 Cocaine 1000 25 2500 100 1000 Cocaethylene 1000 25 2500 100 1000 Cotinine 1000 25 2500 100 1000 Cannabidiol 1000 25 2500 100 1000 Caffeine 1000 25 2500 100 1000 Carisoprodol 1000 25 2500 100 1000 Clonazepam 1000 25 2500 100 1000 Dextromethorphan 1000 25 2500 100 1000 Dihydrocodeine 1000 25 2500 100 1000 Dextrorphan 1000 25 2500 100 1000 Desmethyltapentadol 1000 25 2500 100 1000 10,11-Dihydro-10-Hydroxycarbamazepine 1000 25 2500 100 1000 Diazepam 1000 25 2500 100 1000 Desalkylflurazepam 1000 25 2500 100 1000 Ecgonine methyl ester 1000 25 2500 100 1000 Ephedrine 1000 25 2500 100 1000 EDDP 1000 25 2500 100 1000 Flurazepam 1000 25 2500 100 1000 Fentanyl 1000 25 2500 100 1000 2-Hydroxyethylflurazepam 1000 25 2500 100 1000 Gabapentin 1000 25 2500 100 1000 ULLOQ 5,000 ng/ml Hydrocodone 1000 25 2500 100 1000 Hydromorphone 1000 25 2500 100 1000 THC-OH 1000 25 2500 100 1000 Hydroxymidazolam 1000 25 2500 100 1000 Ketamine 1000 25 2500 100 1000 Lorazepam 1000 25 2500 100 1000 Morphine 1000 25 2500 100 1000 Methadone 1000 25 2500 100 1000 Methamphetamine 1000 25 2500 100 1000 MDA 1000 25 2500 100 1000 MDMA 1000 25 2500 100 1000 Meperidine 1000 25 2500 100 1000 Meprobamate 1000 25 2500 100 1000 Methylone 1000 25 2500 100 1000 MDVP 1000 25 2500 100 1000 4-Methylephedrine 1000 25 2500 100 1000 Midazolam 1000 25 2500 100 1000 Norcodeine 1000 25 2500 100 1000 Noroxycodone 1000 25 2500 100 1000 Norfentanyl 1000 25 2500 100 1000 Norketamine 1000 25 2500 100 1000 Nalbuphine 1000 25 2500 100 1000 Norhydrocodone 1000 25 2500 100 1000 Norbuprenorphine 1000 25 2500 100 1000 Normeperidine 1000 25 2500 100 1000 Norhydromorphone 1000 25 2500 100 1000 Nordiazepam 1000 25 2500 100 1000 Oxycodone 1000 25 2500 100 1000 Oxymorphone 1000 25 2500 100 1000 Oxazepam 1000 25 2500 100 1000 PCP 1000 25 2500 100 1000 Phenobarbital 1000 25 2500 100 1000 Pentobarbital* 1000 12.5 2500 100 1000 *Amo-Pento Phentermine 1000 25 2500 100 1000 Pregabalin 1000 25 2500 100 1000 ULLOQ 2,500 ng/ml Pentazocine 1000 25 2500 100 1000 Secobarbital 1000 25 2500 100 1000 Sufentanil 100 250 2500 100 100 100 mg/ml THC 1000 25 2500 100 1000 THC-COOH 1000 25 2500 100 1000 Tramadol 1000 25 2500 100 1000 O-Desmethyl-cis-tramadol 1000 25 2500 100 1000 Tapentadol 1000 25 2500 100 1000 Temazepam 1000 25 2500 100 1000 Triazolam 1000 25 2500 100 1000 Hydroxytriazolam 1000 25 2500 100 1000 Zaleplon 1000 25 2500 100 1000 Amount of the above analytes (µL 2250 Amount of Methanol to Add (µL) 7750 Total Volume 10000 85
*Note: Amobarbital and Pentobarbital are isomers and coelute. So individual cutoffs were combined to give 100 ng/mL for Amo-Pentobarbital mix.
Total or final volume is brought up to 10 ml in volumetric flask with methanol 86
APPENDIX E. LOW QC STOCK SOLUTION PREP GUIDE
Low QC Stock Solution (LoQC_SS) Preparation - Expiration = 1 yr (Freezer) Analyte Stock Concentration (µg/mL) Spike Volume (µL) LoQC SS = 60 CO Cutoff (ng/mL) Initial Stock Conc. µg/mL Comments Amphetamine 1000 6 60 1 1000 6-MAM 1000 6 60 1 1000 Amobarbital* 1000 3 60 1 1000 6-MAC 1000 6 60 1 1000 Alfentanil 1000 6 60 1 1000 Alprazolam 1000 6 60 1 1000 Alpha-Hydroxyalprazolam 1000 6 60 1 1000 7-Aminoclonazepam 1000 6 60 1 1000 Refrigerator Benzoylecgonine 1000 6 60 1 1000 Butalbital 1000 6 60 1 1000 Butabarbital 1000 6 60 1 1000 Bupropion 1000 6 60 1 1000 Buprenorphine 1000 6 60 1 1000 Codeine 1000 6 60 1 1000 Cocaine 1000 6 60 1 1000 Cocaethylene 1000 6 60 1 1000 Cotinine 1000 6 60 1 1000 Cannabidiol 1000 6 60 1 1000 Caffeine 1000 6 60 1 1000 Carisoprodol 1000 6 60 1 1000 Clonazepam 1000 6 60 1 1000 Dextromethorphan 1000 6 60 1 1000 Dihydrocodeine 1000 6 60 1 1000 Dextrorphan 1000 6 60 1 1000 Desmethyltapentadol 1000 6 60 1 1000 10,11-Dihydro-10-Hydroxycarbamazepine 1000 6 60 1 1000 Diazepam 1000 6 60 1 1000 Desalkylflurazepam 1000 6 60 1 1000 Ecgonine methyl ester 1000 6 60 1 1000 Ephedrine 1000 6 60 1 1000 EDDP 1000 6 60 1 1000 Flurazepam 1000 6 60 1 1000 Fentanyl 1000 6 60 1 1000 2-Hydroxyethylflurazepam 1000 6 60 1 1000 Gabapentin 1000 6 60 1 1000 Hydrocodone 1000 6 60 1 1000 Hydromorphone 1000 6 60 1 1000 THC-OH 1000 6 60 1 1000 Alpha-Hydroxymidazolam 1000 6 60 1 1000 Ketamine 1000 6 60 1 1000 Lorazepam 1000 6 60 1 1000 Morphine 1000 6 60 1 1000 Methadone 1000 6 60 1 1000 Methamphetamine 1000 6 60 1 1000 MDA 1000 6 60 1 1000 MDMA 1000 6 60 1 1000 Meperidine 1000 6 60 1 1000 Meprobamate 1000 6 60 1 1000 Methylone 1000 6 60 1 1000 MDVP 1000 6 60 1 1000 4-Methylephedrine 1000 6 60 1 1000 Midazolam 1000 6 60 1 1000 Norcodeine 1000 6 60 1 1000 Noroxycodone 1000 6 60 1 1000 Norfentanyl 1000 6 60 1 1000 Norketamine 1000 6 60 1 1000 Nalbuphine 1000 6 60 1 1000 Norhydrocodone 1000 6 60 1 1000 Norbuprenorphine 1000 6 60 1 1000 Normeperidine 1000 6 60 1 1000 Norhydromorphone 1000 6 60 1 1000 Nordiazepam 1000 6 60 1 1000 Oxycodone 1000 6 60 1 1000 Oxymorphone 1000 6 60 1 1000 Oxazepam 1000 6 60 1 1000 PCP 1000 6 60 1 1000 Phenobarbital 1000 6 60 1 1000 Pentobarbital* 1000 3 60 1 1000 Phentermine 1000 6 60 1 1000 Pregabalin 1000 6 60 1 1000 Pentazocine 1000 6 60 1 1000 Secobarbital 1000 6 60 1 1000 Sufentanil 100 60 60 1 100 100 mg/ml THC 1000 6 60 1 1000 THC-COOH 1000 6 60 1 1000 Tramadol 1000 6 60 1 1000 O-Desmethyl-cis-tramadol 1000 6 60 1 1000 Tapentadol 1000 6 60 1 1000 Temazepam 1000 6 60 1 1000 Triazolam 1000 6 60 1 1000 Alpha-Hydroxytriazolam 1000 6 60 1 1000 Zaleplon 1000 6 60 1 1000 Amount of the above analytes (µL) 540 Amount of Methanol to Add (µL) 99460 Total Volume 100000 87
*Note: Amobarbital and Pentobarbital are isomers and coelute. So individual cutoffs were combined to give 100 ng/mL for Amo-Pentobarbital mix.
Sub-stock solutions prepared in 100%MeOH
Total or final volume is brought up to 100 ml in volumetric flask with methanol 88
APPENDIX F. INTERNAL STANDARD STOCK SOLUTION PREP GUIDE
Internal Standards Stock Solution (ISTD_SS) - Expiration = 2 Months (Freezer) Internal Standard Stock Concentration (mg/mL) 25 ml final volume (mL) 50 ml final volume (mL) 125 ml final volume (mL) Comments 6-MAM-D3 100 25 50 125 Amphetamine-D5 100 15 30 75 Benzoylecgonine-D3 100 5 10 25 Buprenorphine-D4 100 25 50 125 Butabarbital-D5 100 85 170 425 Caffeine-13C-D3 100 5 10 25 Cannabidiol-D3 100 85 170 425 Cocaethylene-D3 100 10 20 100 Cocaine-D3 100 10 20 100 Codeine-D3 100 60 120 300 Cotinine-D3 100 5 10 25 Diazepam-D5 100 10 20 50 EDDP-D3 100 5 10 25 Fentanyl-D5 100 5 10 25 Hydrocodone-D6 100 5 10 25 Hydromorphone-D6 100 20 40 100 Ketamine-D4 100 5 10 25 Lorazepam-D4 100 15 30 75 Meperidine-D4 100 5 10 25 Methadone-D3 100 10 20 50 Methamphetamine-D5 100 10 20 50 Midazolam-D4 100 5 10 25 Morphine-D3 100 25 50 125 Norbuprenorphine-D3 100 40 80 200 Norcodeine-D3 1000 25 50 125 Substock Nordiazepam-D5 100 25 50 125 Norhydrocodone-D3 100 15 30 75 Norketamine-D4 100 15 30 75 Normeperidine-D4 100 5 10 25 Noroxycodone-D3 100 25 50 125 Oxazepam-D5 100 5 10 25 Oxycodone-D6 100 35 70 175 PCP-D5 100 5 10 25 Pentobarbital-D5 100 90 180 450 Phenobarbital-D5 100 140 280 700 Tapentadol-D3 100 5 10 25 THCCOOH-D9 100 85 170 425 THC-D3 1000 115 230 575 THC-OH-D3 100 85 170 425 Zaleplon-D4 100 15 30 75 Amount of combined Internal Standards in (µL) 1180 2360 6000 Substock: 9:1 MeOH:ISTD Stock MeOH 23820 47640 94000 Total Volume (µl) 25000 50000 100000
Note: Resulting concentrations are based on a 25 mL, 50 mL, or 100 mL final volume 89
APPENDIX G. CALIBRATION CURVE STOCK AND QC STOCK PREP GUIDE
Calibration Curve Stock (CCS) and QC Stock (QCS) Preparation Prepare all solutions in Amber Glass vials and protect from light.
MeOH Stocks - Expiration = 2 Months (Freezer) Calibration Curve Stock (CCS) Calibration Curve Stock Solution Used Spike Volume (µL) MeOH Volume (µL) Final Volume (µL) 0.40X 10.0X 40 960 1000 0.75X 10.0X 75 925 1000 CO 10.0X 100 900 1000 2.0X 10.0X 200 800 1000 3.0X 10.0X 300 700 1000 10.0X CCSS 360 1440 1800 50.0X/100.0X CCSS 1000 0 1000 Quality Control Stock Solution Used Spike Volume (µL) MeOH Volume (µL) Final Volume (µL) HiQC HiQC_SS 1500 0 1500 LoQC LoQC_SS 1500 0 1500 NegQC 100% Methanol 0 1500 1500
Note: Spike volumes calculated off desired final volume as an input 90
APPENDIX H. ARTIFICIAL SERUM CALIBRATORS AND QC PREP GUIDE
Artificial Serum (AS) Calibrators and Controls Preparation Prepare all solutions in Amber Glass vials and protect from light. Artificial Serum Stocks - Expiration = 48 hours (Frozen) Calibration Standard Solution Used Spike Volume (µL) AS (µL) Final Volume (µL) 0.40X CCS-0.40X 10 990 1000 0.75X CCS-0.75X 10 990 1000 CO CCS-1.0X 10 990 1000 2.0X CCS-2.0X 10 990 1000 3.0X CCS-3.0X 10 990 1000 10.0X CCS-10.0X 10 990 1000 50.0X CCSS 10 990 1000 100.0X CCSS 20 980 1000 Quality Control Solution Used Spike Volume (µL) AS (µL) Final Volume (µL) HiQC QCS-HiQC 10 990 1000 LoQC QCS-LoQC 10 990 1000 NegQC QCS-Neg 10 990 1000 Quality Control Solution Used Spike Volume (µL) AS (µL) Final Volume (µL) SST (CO) CCS-1.0X 10 990 1000 30%(CO) QCS-LoQC 5 995 1000
AS is artificial serum CO is cutoff (1ng/mL) SST is a system suitability test 30% CO is 0.3 ng/mL 91
APPENDIX I. BATCH FILE EXAMPLE 92
APPENDIX J. STANDARD ANALYTES WITH THEIR INTERNAL STANDARD ASSOCIATIONS
Standard Analyte Internal Standard Association Standard Analyte Internal Standard Association 10,11-Dihydro-10-Hydroxycarbamazepine Norbuprenorphine-D3 MDMA Methamphetamine-D5 4-Methylephedrine Methamphetamine-D5 MDVP Benzoylecgonine-D3 2-Hydroxyethylflurazepam Nordiazepam-D5 Meperidine Meperidine-D4 6-MAC Benzoylecgonine-D3 Meprobamate Benzoylecgonine-D3 6-MAM 6-MAM-D3 Methadone Methadone-D3 7-Aminoclonazepam Norbuprenorphine-D3 Methamphetamine Methamphetamine-D5 Alfentanil Buprenorphine-D4 Methylone Methamphetamine-D5 Alprazolam Diazepam-D5 Midazolam Midazolam-D4 Amobarbital Pentobarbital-D5 Morphine Morphine-D3 Amphetamine Amphetamine-D5 Nalbuphine Tapentadol-D3 Benzoylecgonine Benzoylecgonine-D3 Norbuprenorphine Norbuprenorphine-D3 Buprenorphine Buprenorphine-D4 Norcodeine Norcodiene-D3 Bupropion Benzoylecgonine-D3 Nordiazepam Nordiazepam-D5 Butabarbital Butabarbital-D5 Norfentanyl Norketamine-D4 Butalbital Butabarbital-D5 Norhydrocodone Norhydrocodone-D3 Caffeine Caffeine-13C-D3 Norhydromorphone Morphine-D3 Cannabidiol Cannabidiol-D3 Norketamine Norketamine-D4 Carisoprodol Midazolam-D4 Normeperidine Normeperidine-D4 Clonazepam Oxazepam-D5 Noroxycodone Noroxycodone-D3 Cocaethylene Cocaethylene-D3 O-Desmethyl-cis-tramadol Codeine-D3 Cocaine Cocaine-D3 Oxazepam Oxazepam-D5 Codeine Codeine-D3 Oxycodone Oxycodone-D6 Cotinine Cotinine-D3 Oxymorphone Morphine-D3 Desalkylflurazepam Lorazepam-D4 PCP PCP-D5 Desmethyltapentadol Norketamine-D4 Pentazocine Norbuprenorphine-D3 Diazepam Diazepam-D5 Pentobarbital Pentobarbital-D5 Dihydrocodeine Codeine-D3 Phenobarbital Phenobarbital-D5 Ecgonine methyl ester Morphine-D3 Phentermine Methamphetamine-D5 EDDP EDDP-D3 Pregabalin Amphetamine-D5 Ephedrine Hydromorphone-D6 Secobarbital Phenobarbital-D5 Fentanyl Fentanyl-D5 Sufentanil Midazolam-D4 Flurazepam Fentanyl-D5 Tapentadol Tapentadol-D3 Gabapentin Methamphetamine-D5 Temazepam Nordiazepam-D5 Hydrocodone Hydrocodone-D6 THC THC-D3 Hydromorphone Hydromorphone-D6 THC-COOH THC-COOH-D9 Hydroxyalprazolam Midazolam-D4 THC-OH THC-OH-D3 Hydroxymidazolam Midazolam-D4 Tramadol Ketamine-D4 Hydroxytriazolam Midazolam-D4 Triazolam Diazepam-D5 Ketamine Ketamine-D4 Zaleplon Zaleplon-D4 Lorazepam Lorazepam-D4 Dextromethorphan PCP-D5 MDA Mthamphetamine-D5 Dextrorphan Ketamine-D4 93
APPENDIX K. STANDARD ANALYTE METHOD PARAMETERS
Name Ret. Time m/z Ref. Ions Event ISTD Group 10,11-Dihydro-10-Hydroxycarbamazepine 3.601 255.20>194.25 255.20>193.10 0.00 0 70:MRM(+) 20 2-Hydroxyethylflurazepam 5.338 333.20>211.30 333.20>305.25 0.00 0 99:MRM(+) 22 4-Methylephedrine 1.739 180.20>162.30 180.20>147.25 0.00 0 22:MRM(+) 17 6-MAC 3.125 342.10>225.30 342.10>165.30 0.00 0 59:MRM(+) 3 6-MAM 2.027 328.20>165.10 328.20>211.10 0.00 0 33:MRM(+) 1 7-Aminoclonazepam 3.581 286.00>222.10 286.00>250.00 0.00 0 69:MRM(+) 20 Alfentanil 4.353 417.20>268.20 417.20>197.25 0.00 0 77:MRM(+) 4 Alprazolam 5.69 309.00>281.10 309.00>205.10 0.00 0 103:MRM(+) 9 Amo-Pentobarbital 3.945 225.10>182.35 225.10>42.15 0.00 0 114:MRM(-) 30 Amphetamine 1.179 136.20>119.30 136.20>65.25 0.00 0 9:MRM(+) 2 Benzoylecgonine 3.003 290.10>168.20 290.10>82.10 0.00 0 58:MRM(+) 3 Buprenorphine 4.426 468.20>396.15 468.20>414.35 0.00 0 79:MRM(+) 4 Bupropion 2.993 240.20>184.10 240.20>130.10 0.00 0 57:MRM(+) 3 Butabarbital 3.292 211.10>168.30 211.10>42.20 0.00 0 112:MRM(-) 5 Butalbital 3.478 223.10>180.30 223.10>42.15 0.00 0 113:MRM(-) 5 Caffeine 2.567 195.00>138.10 195.00>110.00 0.00 0 43:MRM(+) 36 Cannabidiol 6.378 313.30>245.40 313.30>107.30 0.00 0 122:MRM(-) 6 Carisoprodol 4.597 261.10>176.25 261.10>62.05 0.00 0 83:MRM(+) 18 Clonazepam 5.02 316.00>270.00 316.00>214.10 0.00 0 88:MRM(+) 27 Cocaethylene 3.562 318.10>196.25 318.10>82.25 0.00 0 68:MRM(+) 37 Cocaine 3.137 304.00>182.05 304.00>82.15 0.00 0 62:MRM(+) 38 Codeine 1.981 300.10>152.10 300.10>165.10 0.00 0 30:MRM(+) 7 Cotinine 1.661 177.20>80.20 177.20>98.15 0.00 0 19:MRM(+) 8 Desalkylflurazepam 5.292 289.00>140.15 289.00>226.10 0.00 0 98:MRM(+) 14 Desmethyltapentadol 2.424 208.10>107.25 208.10>51.00 0.00 0 38:MRM(+) 24 Dextromethorphan 4.007 272.00>215.15 272.00>171.10 0.00 0 71:MRM(+) 29 Dextrorphan 2.711 258.30>157.05 258.30>201.10 0.00 0 48:MRM(+) 13 Diazepam 5.953 285.00>193.15 285.00>154.10 0.00 0 106:MRM(+) 9 Dihydrocodeine 1.985 302.10>199.10 302.10>201.20 0.00 0 31:MRM(+) 7 Ecgonine methyl ester 0.365 200.10>82.00 200.10>68.10 0.00 0 1:MRM(+) 19 EDDP 4.455 278.10>234.20 278.10>249.05 0.00 0 81:MRM(+) 39 Ephedrine 1.018 166.20>117.10 166.20>133.10 0.00 0 6:MRM(+) 12 Fentanyl 4.111 337.10>188.15 337.10>132.25 0.00 0 75:MRM(+) 10 Flurazepam 4.207 388.10>315.10 388.10>317.00 0.00 0 76:MRM(+) 10 Gabapentin 1.235 172.20>119.30 172.20>93.25 0.00 0 10:MRM(+) 17 Hydrocodone 2.184 300.10>199.10 300.10>171.20 0.00 0 37:MRM(+) 11 Hydromorphone 1.303 286.20>185.10 286.20>157.10 0.00 0 12:MRM(+) 12 Hydroxyalprazolam 5.26 325.00>297.15 325.00>216.10 0.00 0 95:MRM(+) 18 Hydroxymidazolam 5.161 342.00>202.95 342.00>168.10 0.00 0 93:MRM(+) 18 Hydroxytriazolam 5.262 358.90>331.00 359.10>176.10 0.00 0 96:MRM(+) 18 Ketamine 2.813 238.00>125.25 238.00>207.15 0.00 0 50:MRM(+) 13 Lorazepam 5.061 321.00>229.20 321.00>194.25 0.00 0 92:MRM(+) 14 MDA 1.519 180.20>135.05 180.20>132.90 0.00 0 13:MRM(+) 17 MDMA 1.857 194.20>163.15 194.20>133.15 0.00 0 26:MRM(+) 17 MDPV 3.199 276.10>126.20 276.10>135.10 0.00 0 63:MRM(+) 3 Meperidine 2.957 248.20>220.10 248.20>174.20 0.00 0 55:MRM(+) 15 Meprobamate 3.128 219.25>158.10 219.25>97.25 0.00 0 60:MRM(+) 3 Methadone 4.844 310.30>265.30 310.30>117.30 0.00 0 85:MRM(+) 16 Methamphetamine 1.586 150.10>119.20 150.10>65.20 0.00 0 17:MRM(+) 17 Methylone 1.661 208.00>132.05 208.00>159.95 0.00 0 20:MRM(+) 17 Midazolam 4.861 326.00>291.00 326.00>209.10 0.00 0 87:MRM(+) 18 Morphine 0.984 286.20>151.90 286.20>165.20 0.00 0 5:MRM(+) 19 Nalbuphine 2.676 358.10>185.10 358.10>254.10 0.00 0 47:MRM(+) 33 Norbuprenorphine 3.488 413.90>165.20 413.90>396.40 0.00 0 66:MRM(+) 20 Norcodeine 1.555 286.20>152.10 286.20>165.10 0.00 0 15:MRM(+) 21 Nordiazepam 5.473 271.00>140.10 271.00>165.10 0.00 0 101:MRM(+) 22 Norfentanyl 2.526 233.20>84.35 233.20>56.05 0.00 0 41:MRM(+) 24 Norhydrocodone 1.859 286.20>199.00 286.20>171.10 0.00 0 27:MRM(+) 23 Norhydromorphone 0.716 272.20>185.10 272.20>157.10 0.00 0 2:MRM(+) 19 Norketamine 2.522 224.00>125.15 224.00>207.15 0.00 0 40:MRM(+) 24 Normeperidine 2.871 234.20>160.15 234.20>56.20 0.00 0 53:MRM(+) 25 Noroxycodone 1.753 302.20>187.10 302.20>198.20 0.00 0 24:MRM(+) 26 O-Desmethyl-cis-Tramadol 1.968 250.30>58.30 250.30>42.30 0.00 0 28:MRM(+) 7 Oxazepam 5.038 287.10>104.25 287.10>163.25 0.00 0 90:MRM(+) 27 Oxycodone 2.092 316.20>241.20 316.20>256.20 0.00 0 35:MRM(+) 28 Oxymorphone 1.106 302.00>227.20 302.00>198.05 0.00 0 7:MRM(+) 19 PCP 4.072 244.30>86.20 244.30>159.20 0.00 0 73:MRM(+) 29 Pentazocine 3.482 286.10>218.20 286.10>69.15 0.00 0 65:MRM(+) 20 Phenobarbital 3.176 231.00>42.20 231.00>188.30 0.00 0 110:MRM(-) 31 Phentermine 1.692 150.20>133.10 150.20>65.10 0.00 0 21:MRM(+) 17 Pregabalin 0.845 160.20>83.30 160.20>97.30 0.00 0 3:MRM(+) 2 Secobarbital 4.259 237.10>194.40 237.10>42.15 0.00 0 116:MRM(-) 31 Sufentanil 4.596 387.10>238.25 387.10>111.15 0.00 0 82:MRM(+) 18 Tapentadol 2.627 222.20>107.15 222.20>103.10 0.00 0 46:MRM(+) 33 Temazepam 5.618 301.10>177.20 301.00>193.30 0.00 0 102:MRM(+) 22 THC 6.579 315.10>193.35 315.10>123.25 0.00 0 108:MRM(+) 40 THC-COOH 6.369 343.10>245.30 343.10>191.30 0.00 0 120:MRM(-) 34 THC-OH 6.27 329.30>268.35 329.30>173.35 0.00 0 118:MRM(-) 41 Tramadol 2.824 264.30>58.30 264.30>42.30 0.00 0 51:MRM(+) 13 Triazolam 5.7 342.90>308.10 342.90>315.00 0.00 0 104:MRM(+) 9 Zaleplon 5.265 306.20>236.30 306.20>264.30 0.00 0 97:MRM(+) 35 94
APPENDIX L. INTERNAL STANDARD METHOD PARAMETERS
Name Ret. Time m/z Event ISTD Group 6-MAM-D3 2.021 331.00>165.10 32:MRM(+) 1 Amphetamine-D5 1.162 141.10>124.20 8:MRM(+) 2 Benzoylecognine-D3 2.992 293.20>171.05 56:MRM(+) 3 Buprenorphine-D4 4.392 472.20>59.20 78:MRM(+) 4 Butabarbital-D5 3.264 216.00>173.15 111:MRM(-) 5 Caffeine-13C-D3 2.544 198.90>142.15 42:MRM(+) 36 Cannabidiol-D3 6.374 316.00>248.15 121:MRM(-) 6 Cocaethylene-D3 3.557 321.10>199.15 67:MRM(+) 37 Cocaine-D3 3.132 307.10>185.15 61:MRM(+) 38 Codeine-D3 1.971 302.95>165.10 29:MRM(+) 7 Cotinine-D3 1.649 180.10>80.00 18:MRM(+) 8 Diazepam-D5 5.939 290.00>198.10 105:MRM(+) 9 EDDP-D3 4.449 281.10>234.10 80:MRM(+) 39 Fentanyl-D5 4.093 342.30>188.20 74:MRM(+) 10 Hydrocodone-D6 2.16 306.00>201.95 36:MRM(+) 11 Hydromorphone-D6 1.28 292.20>185.10 11:MRM(+) 12 Ketamine-D4 2.794 242.20>129.10 49:MRM(+) 13 Lorazepam-D4 5.041 327.00>281.20 91:MRM(+) 14 Meperidine-D4 2.946 252.10>224.20 54:MRM(+) 15 Methadone-D3 4.836 313.10>268.15 84:MRM(+) 16 Methamphetamine-D5 1.572 155.10>121.20 16:MRM(+) 17 Midazolam-D4 4.845 330.20>295.30 86:MRM(+) 18 Morphine-D3 0.971 289.10>152.10 4:MRM(+) 19 Norbuprenorphine-D3 3.477 417.20>101.05 64:MRM(+) 20 Norcodeine-D3 1.544 289.10>152.10 14:MRM(+) 21 Nordiazepam-D5 5.449 276.00>140.10 100:MRM(+) 22 Norhydrocodone-D3 1.843 289.10>202.10 25:MRM(+) 23 Norketamine-D4 2.503 228.20>129.10 39:MRM(+) 24 Normeperidine-D4 2.862 238.10>164.20 52:MRM(+) 25 Noroxycodone-D3 1.739 305.20>230.20 23:MRM(+) 26 Oxazepam-D5 5.021 292.00>246.15 89:MRM(+) 27 Oxycodone-D6 2.07 322.10>247.20 34:MRM(+) 28 PCP-D5 4.052 249.30>86.20 72:MRM(+) 29 Pentobarbital-D5 3.954 230.00>187.20 115:MRM(-) 30 Phenobarbital-D5 3.145 236.00>193.15 109:MRM(-) 31 Tapentadol-D3 2.621 225.10>107.15 45:MRM(+) 33 THC-COOH-D9 6.353 352.20>254.20 119:MRM(-) 34 THC-D3 6.574 318.25>196.25 107:MRM(+) 40 THC-OH-D3 6.264 332.20>271.30 117:MRM(-) 41 Zaleplon-D4 5.253 310.20>240.30 94:MRM(+) 35