DOCSLIB.ORG

Simulating the Drug Discovery Pipeline: a Monte Carlo Approach Melvin J Yu

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Yu Journal of Cheminformatics 2012, 4:32 http://www.jcheminf.com/content/4/1/32 RESEARCH ARTICLE Open Access Simulating the drug discovery pipeline: a Monte Carlo approach Melvin J Yu Abstract Background: The early drug discovery phase in pharmaceutical research and development marks the beginning of a long, complex and costly process of bringing a new molecular entity to market. As such, it plays a critical role in helping to maintain a robust downstream clinical development pipeline. Despite its importance, however, to our knowledge there are no published in silico models to simulate the progression of discrete virtual projects through a discovery milestone system. Results: Multiple variables were tested and their impact on productivity metrics examined. Simulations predict that there is an optimum number of scientists for a given drug discovery portfolio, beyond which output in the form of preclinical candidates per year will remain flat. The model further predicts that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity. Conclusions: The model may be useful as a tool to facilitate analysis of historical growth and achievement over time, help gauge current working group progress against future performance expectations, and provide the basis for dialogue regarding working group best practices and resource deployment strategies. Background The study by Paul and co-workers1 indicates that the Over the past fifteen years, productivity of pharmaceut- lead optimization phase of drug discovery contributes ical research and development (R&D) in terms of significantly toward the out-of-pocket cost per launch of launched new molecular entities (NME) has at best been an NME. Yet, despite the importance of the discovery flat while costs have risen, resulting in a sharp decline in phase in pharmaceutical R&D, studies are lacking in the the number of first-in-class drugs entering the market as literature to provide guidance regarding the optimal dis- a percentage of R&D expenditures [1,2]. Adoption of tribution of scientific resources to generate a sufficient new technologies, six sigma initiatives [3,4] and adaptive quantity of preclinical candidate compounds at a rate clinical trial designs [5,6] have led to incremental that would lead to an NME entering the marketplace in improvements, but the productivity challenge facing the a specific time-frame. While particular sub-milestones pharmaceutical industry as a whole remains. Although and milestone names may vary between the major studies in this area largely focus on the clinical develop- pharmaceutical companies, the primary transition and ment aspect of the pipeline, a steady supply of quality go/no-go decision points for the early drug discovery preclinical drug candidates must be maintained at the pipeline can be categorized as those depicted in front end to prevent downstream gaps from forming. Figure 1. For example, shifting the focus from late stage phase III Attrition rates for compounds as they move through studies to early clinical proof of concept (POC) would the milestone system have been published1 and could be require an abundance of preclinical candidate com- used to approximate the average percent success rate of pounds that both add projected value to the company’s projects transitioning from one stage to the next. The portfolio and have a reasonable probability of technical question that we asked was whether this information success. could be employed to derive useful guidelines for human resource distribution by scientific discipline, and what Correspondence: [email protected] the optimal number of chemists and biologist might be Eisai Inc., 4 Corporate Dr., Andover, MA 01810, USA © 2012 Yu; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Yu Journal of Cheminformatics 2012, 4:32 Page 2 of 14 http://www.jcheminf.com/content/4/1/32 values. Since these studies are usually, if not exclusively, performed on candidate compounds and not during the early phase of drug discovery, we assume for purposes of Figure 1 Diagram of major milestone transition and go/no-go the model that chemistry, biology and discovery DMPK decision points in the discovery phase of pharmaceutical R&D represent the rate limiting scientific disciplines during leading to preclinical development. Details as follows: (1) Conduct early discovery, depending on the scientific issues to be exploratory work that includes project conception, target validation addressed. Although data generated by the other specia- and screening. (2) Identify chemical lead(s) and establish target drug lized disciplines are certainly used by project teams to profile. (3) Optimize chemical properties and biological activity against the target drug profile. (4) Complete all necessary studies to help guide a structure-activity relationship (SAR) investi- allow an investigational new drug (IND) application to be filed. gation, the model assumes that there is enough resource support from the ancillary disciplines to allow decisions to be made within the discovery cycle times specified by to support a given portfolio of discovery project types the user. under a specific set of conditions. The algorithm takes as input a series of user-defined Although a number of simulation and game theory numbers that describes a typical project at the hit to lead algorithms have been used in clinical development mod- and lead optimization stages. This includes a target els, [7] to our knowledge there are no published in silico number of chemists and biologists, cycle time, milestone models that simulate the progression of projects through transition probabilities, and project type. Project type in- early discovery, i.e. from conceptualization to candidate clude those that are biology-driven (i.e., projects that selection. Given the importance of the discovery phase typically involve high-throughput screening (HTS) to pharmaceutical R&D, we developed a Monte Carlo against a biological target of interest), and those that are simulation algorithm for modeling discrete virtual pro- chemistry-driven with a clearly identified chemical lead, jects as they move through the discovery milestone sys- but not necessarily information about mechanism of ac- tem ending with entry into preclinical development. tion (MOA). In either case, the link between MOA and Each virtual project has associated with it a dynamic human disease may or may not be established. Another number of chemists and biologists with drug metabol- type of project are those that are “follow-on” or “back- ism/pharmacokinetics (DMPK) support that varies up” projects that share biological resources and thera- according to project priority, type, and milestone stage. peutic target with another on-going discovery program, Such an in silico model would allow multiple variables albeit with a different structural scaffold or chemical to be evaluated for a given portfolio of drug discovery lead. In such cases, the SAR will likely diverge from its programs in the context of expected productivity related project, and will, if successful, afford a candidate metrics. compound with a similar drug profile, but with a differ- ent scaffold and therefore a different set of physico- Methods chemical properties. The algorithm treats the two as With scientific specialization comes the need for cross- separate and distinct projects, albeit with different levels functional teams [8]. Pharmaceutical R&D is no excep- of chemical and biological support, whose outcome tion. For purposes of the model, however, a simplifying could both lead to preclinical candidate compounds. assumption is made that drug discovery project teams The user has the ability to specify the percent of projects consist of medicinal/synthetic chemists, biologists cap- that are “follow-on” and the percent of those that are able of developing and running multi-tiered in vitro chemistry versus biology driven (Figure 2). assay systems and relevant in vivo animal models as well Since project cycle time is dependent on the level of as representation by members of the DMPK group. resource support, a simple monotonic function was used While scientists from a number of specialized scientific to correct the time to milestone decision. For example, disciplines (e.g. process chemistry research, analytical the algorithm may allow a project at the hit to lead stage chemistry, computational chemistry, biopharmaceutical to reach lead optimization in one year if fully staffed, but assessment, formulation, in vivo imaging, etc.) partici- would correct that value to two years if only half-staffed. pate to varying degrees in the drug discovery process, If additional resources become available in-between the their roles are assumed not to be rate limiting with re- hit to lead and lead optimization stages, then the time to gard to decision-making until projects approach the pre- milestone decision would be adjusted according to clinical development go/no-go decision point. Cost and Equation 1. In this fashion, project
Recommended publications
  • On the Organization of a Drug Discovery Platform

    On the Organization of a Drug Discovery Platform

    DOI: 10.5772/intechopen.73170 ProvisionalChapter chapter 2 On the Organization of a Drug Discovery Platform Jean A. Boutin,A. Boutin, Olivier NosjeanOlivier Nosjean and Gilles FerryGilles Ferry Additional information is available at the end of the chapter http://dx.doi.org/10.5772/intechopen.73170 Abstract Some of the most exciting parts of work in the pharmaceutical industry are the steps lead- ing up to drug discovery. This process can be oversimplified by describing it as a screen- ing campaign involving the systematic testing of many compounds in a test relevant to a given pathology. This naïve description takes place without taking into consideration the numerous key steps that led up to the screening or the steps that might follow. The present chapter describes this whole process as it was conducted in our company dur- ing our early drug discovery activities. First, the purpose of the procedures is described and rationalized. Next follows a series of mostly published examples from our own work illustrating the various steps of the process from cloning to biophysics, including expression systems and membrane-bound protein purifications. We believe that what is described here presents an example of how pharmaceutical industry research can orga- nize its platform(s) when the goal is to find and qualify a new preclinical drug candidate using cutting-edge technologies and a lot of hard work. Keywords: drug discovery, validation, cloning & expression, biophysics, structural biology, organization 1. Introduction Drug discovery involves a suite of processes as part of a program aimed at finding drug thera- pies for diseases. These programs encompass many different scientific steps from validation of the target (or attempts to do so) and characterization of the hits until the selection of can- didates for medicinal chemistry programs.
  • Predicting Strategies for Lead Optimization Via Learning to Rank

    Predicting Strategies for Lead Optimization Via Learning to Rank

    IPSJ Transactions on Bioinformatics Vol.11 41–47 (Dec. 2018) [DOI: 10.2197/ipsjtbio.11.41] Original Paper Predicting Strategies for Lead Optimization via Learning to Rank Nobuaki Yasuo1,2,a) Keisuke Watanabe1 Hideto Hara3 Kentaro Rikimaru3 Masakazu Sekijima1,4,b) Received: August 21, 2018, Accepted: September 18, 2018 Abstract: Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding affinity, target selectivity, physicochemical properties, and tox- icity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods. Keywords: lead optimization, learning to rank, computer-aided drug design, machine learning computer-aided drug discovery (CADD), which has been utilized 1. Introduction since the 1960s, are also leading current drug discovery. The During drug discovery, enormous attempts are being made to methods of CADD can be combined with various biological data identify better drug candidates. Since the cost of drug discovery including genomic sequence, protein tertiary structure, and chem- has been drastically increased, recently the process of drug dis- ical structure, and can be utilized in various steps in drug discov- covery typically takes 12–14 years [1] and costs approximately ery: target identification, compound screening, and ADME (ab- 2.6 billion USD [2]. The process of drug discovery is sometimes sorption, distribution, metabolism, excretion, toxicity) properties likened to looking for a needle in a haystack; it is the process prediction [9], [10], [11].
  • Medicinal Chemistry for Drug Discovery | Charles River

    Medicinal Chemistry for Drug Discovery | Charles River

    Summary Medicinal chemistry is an integral part of bringing a drug through development. Our medicinal chemistry approach enables clients to benefit from efficient navigation of the early drug discovery process through to delivery of preclinical candidates. DISCOVERY Click to learn more Medicinal Chemistry for Drug Discovery Medicinal Chemistry A Proven Track Record in Drug Discovery Services: Our medicinal chemistry team has experience in progressing small molecule drug discovery programs across a broad range • Target identification of therapeutic areas and gene families. Our scientists are skilled in the design and synthesis of novel pharmacologically active - Capture Compound® mass compounds and understand the challenges facing modern drug discovery. Together, they are cited as inventors on over spectrometry (CCMS) 350 patents and have identified 80 preclinical candidates for client organizations across a variety of therapeutic areas. As • Hit-finding strategies project leaders, our chemists are fundamental in driving the program strategy and have consistently empowered our clients’ - Optimizing high-throughput success. A high proportion of candidates regularly progress to the clinic, and our first co-invented drug, Belinostat, received screening (HTS) hits marketing approval in 2015. As an organization, Charles River has worked on 85% of the therapies approved in 2018. • Hit-to-lead We have a deep understanding of the factors that drive medicinal chemistry design: structure-activity relationship (SAR), • Lead optimization biology, physical chemistry, drug metabolism and pharmacokinetics (DMPK), pharmacokinetic/pharmacodynamic (PK/PD) • Patent strategy modelling, and in vivo efficacy. Charles River scientists are skilled in structure-based and ligand-based design approaches • Preparation for IND filing utilizing our in-house computer-aided drug design (CADD) expertise.
  • Preclinical Toxicology – Points to Consider in Program Design

    Preclinical Toxicology – Points to Consider in Program Design

    To view this booklet online, please visit PacificBioLabs.com. Preclinical Toxicology – Points To Consider in Program Design PACIFIC BIOLABS – YOUR PARTNER FOR PRECLINICAL SAFETY TESTING As The Service Leader in Bioscience Testing, Pacific BioLabs (PBL) strives to help our clients deliver safe and effective pharmaceuticals to the patients who need them. Well designed and executed preclinical studies are critical to the success of any drug development program. They must reliably assess the safety of a new drug entity, laying the groundwork for clinical trials and ultimately, regulatory approval. Pacific BioLabs interacts closely with our clients, providing quality nonclinical testing results to meet regulatory requirements and guide your drug development decisions. As part of our commitment to clients, we have prepared a pair of publications that will assist you in planning your preclinical testing program. This publication, Preclinical Toxicology – Points to Consider in Program Design, gives an overview of the drug development process, describes the contents of a typical Common Technical Document, shows the relative timing of various studies required for a successful IND and NDA, and presents advice on selecting and working with contract toxicology labs and other CROs. PBL’s companion publication, Preclinical Toxicology – Guidance for Industry – ICH Guidances, is available on our website at PacificBioLabs.com. It presents two major ICH guidance documents that directly address safety testing of new pharmaceuticals: Guidance M3 – Nonclincal Safety Studies for the Conduct of Human Clinical Trials for Pharmaceutical and Guidance S6 – Preclinical Safety Evaluation of Biotechnology-Derived Pharmaceuticals. The FDA website http://www.fda.gov/cder/guidance contains these two documents along with a variety of other references on regulatory expectations for the nonclinical development of NCEs.
  • DMPK) Studies Are Critical to Efficient Drug Discovery Programs and Successful Delivery of Candidate Molecules

    DMPK) Studies Are Critical to Efficient Drug Discovery Programs and Successful Delivery of Candidate Molecules

    Summary Discovery drug metabolism and pharmacokinetics (DMPK) studies are critical to efficient drug discovery programs and successful delivery of candidate molecules. DISCOVERY Drug Metabolism and Pharmacokinetics (DMPK) Click to learn more Supporting Discovery Research DMPK Services The identification and inclusion of appropriate DMPK studies is key to the success of discovery research by helping to de-risk • In vitro ADME candidate molecules and improve project productivity through more targeted chemical synthesis and progression of the right compounds. Charles River’s flexible and collaborativeDMPK team offers a variety of partnerships and pricing structures to suit • Physicochemical properties client needs. In addition to fee-for-service assays and expertise, we can embed our DMPK scientists within existing integrated • Metabolic stability chemistry programs as core team members of multidisciplinary project teams. We offer a wealth of experience in a range of • Drug-drug interactions therapeutic areas and biological targets and can help support strategy and implementation of appropriate screening cascades. • Distribution • Safety • High-throughput and automation Chemistry • Pharmacokinetics (PK) support • Bioanalysis and Biology pharmacokinetic support DMPK Questions for our chemists? Visit https://www.criver.com/ consult-pi-ds-questions-for-our- chemists EVERY STEP OF THE WAY www.criver.com In Vitro ADME As compound potency improves during hit-to-lead and lead optimization, in vitro ADME assays provide necessary data to establish insight into the key physiochemical properties and structural motifs that will provide the targeted candidate profile. Our in vitro ADME scientists have established a suite of assays that routinely support drug discovery programs, continue to work on the development and validation of new assays, and regularly monitor assay performance.
  • Early Hit-To-Lead ADME Screening Bundle Bundled Screening Assays to Accelerate Candidate Selection in Drug Discovery

    Early Hit-To-Lead ADME Screening Bundle Bundled Screening Assays to Accelerate Candidate Selection in Drug Discovery

    Fact Sheet Early Hit-to-Lead ADME Screening Bundle Bundled Screening Assays to Accelerate Candidate Selection in Drug Discovery In vitro ADME screening during the lead optimization stage of drug discovery positively impacts drug candidate selection with an enhanced probability of success in clinical trials. Since most new drug candidates fail during preclinical and clinical development, and the late stage of the drug development cycle can be a lengthy and costly process, any means of identifying drug candidates with optimized ADME and pharmacokinetics properties in the discovery stage will have a significant impact on the drug discovery process overall. Focused on Solutions to Address DMPK Issues and to Enable the Success of Our Clients Our scientists routinely conduct industry standard in vitro metabolism and DDI-based assays, including highly automated ADME in vitro screens. We can help drive your discovery phase structure activity relationship (SAR) by optimizing for ADME properties, in parallel to your receptor binding potency and selectivity, for more rapid identification of high quality drug candidates. Metabolic stability, risk assessment for inhibiting key Cytochrome P450 enzymes, and cell permeability are three main early hit-to-lead ADME screening assays that all new chemical entities (NCEs) are tested for in the industry in effort to optimize key ADME properties. In Vitro ADME Screening Services: Early Hit-to-Lead ADME Screening Bundle Intrinsic Clearance Assay in Liver Microsomes • Liver microsomes; species selectable • Incubation
  • A New Ligand-Based Approach to Virtual Screening and Profiling of Large Chemical Libraries

    A New Ligand-Based Approach to Virtual Screening and Profiling of Large Chemical Libraries

    A new ligand-based approach to virtual screening and profiling of large chemical libraries Elisabet Gregori Puigjané Memòria presentada per optar al grau de Doctor en Biologia per la Universitat Pompeu Fabra. Aquesta Tesi Doctoral ha estat realitzada sota la direcció del Dr. Jordi Mestres al Departament de Ciències Experimentals i de la Salut de la Universitat Pompeu Fabra Jordi Mestres Elisabet Gregori Puigjané Barcelona, Maig 2008 The research in this thesis has been carried out at the Chemogenomics Laboratory (CGL) within the Unitat de Recerca en Informàtica Biomèdica (GRIB) at the Parc de Recerca Biomèdica de Barcelona (PRBB). The research carried out in this thesis has been supported by Chemotargets S. L. Table of contents Acknowledgements ........................................................................................... 3 Abstract .............................................................................................................. 5 Objectives ........................................................................................................... 7 List of publications ............................................................................................ 9 Part I – INTRODUCTION .................................................................................. 11 Chapter I.1. Drug discovery ..................................................................... 13 I.1.1. Obtaining a drug candidate ....................................................... 14 I.1.1.1. Hit identification ..........................................................
  • Research and Development in the Pharmaceutical Industry

    Research and Development in the Pharmaceutical Industry

    Research and Development in the Pharmaceutical Industry APRIL | 2021 At a Glance This report examines research and development (R&D) by the pharmaceutical industry. Spending on R&D and Its Results. Spending on R&D and the introduction of new drugs have both increased in the past two decades. • In 2019, the pharmaceutical industry spent $83 billion dollars on R&D. Adjusted for inflation, that amount is about 10 times what the industry spent per year in the 1980s. • Between 2010 and 2019, the number of new drugs approved for sale increased by 60 percent compared with the previous decade, with a peak of 59 new drugs approved in 2018. Factors Influencing R&D Spending. The amount of money that drug companies devote to R&D is determined by the amount of revenue they expect to earn from a new drug, the expected cost of developing that drug, and policies that influence the supply of and demand for drugs. • The expected lifetime global revenues of a new drug depends on the prices that companies expect to charge for the drug in different markets around the world, the volume of sales they anticipate at those prices, and the likelihood the drug-development effort will succeed. • The expected cost to develop a new drug—including capital costs and expenditures on drugs that fail to reach the market—has been estimated to range from less than $1 billion to more than $2 billion. • The federal government influences the amount of private spending on R&D through programs (such as Medicare) that increase the demand for prescription drugs, through policies (such as spending for basic research and regulations on what must be demonstrated in clinical trials) that affect the supply of new drugs, and through policies (such as recommendations for vaccines) that affect both supply and demand.
  • Drug Discovery and Preclinical Development

    Drug Discovery and Preclinical Development

    Drug Discovery and Preclinical Development Neal G . Simon , Ph . D. Professor Department of Biological Sciences Disclaimer “Those wh o h ave k nowl ed ge, d on’t predi ct . Those who predict, don’t have knowledge.” Lao Tzu, 6th Century BC Chinese Poet Discovery and Preclinical Development I. Background II. The R&D Landscape III. ItidTftiInnovation and Transformation IV. The Preclinical Development Process V. Case Study: Stress-related Affective Disorders Serendipity or Good Science: Building Opportunity Hoffman Osterhof I. Background Drug Development Process Biopharmaceutical Drug Development: Attrition Drug FDA Large Scale Discovery Pre-Clinical Clinical Trials Review Manufacturing / Phase IV Phase I Phase III 20-100 1000-5000 Volunteers Volunteers 10,000 bmitted 1 FDA Com- bmitted 250 Compounds 5 Compounds uu pound uu AdApproved s Drug NDA S NDA IND S Phase II 100-500 Volunteers 5 years 1.5 years 6 years 2 years 2 years Quelle: Burrell Report Biotechnology Industry 2006 Capitalized Cost Estimates per New Molecule *All R&D costs (basic research and preclinical development) prior to initiation of clinical testing ** Based on a 5-year shift and prior growth rates for the preclinical and clinical periods. DiMasi and Grabowski (2007) II. The Research & Developppment Landscape R&D Expenditures and Return on Investment: A Declining Function Phrma (2005); Tufts CSDD (2005) R&D Expenditures 1992-2004 and FDA Approvals Hu et al (2007) NIH Budget by Area Pharmaceutical Industry: Diminishing Returns “That is why the business model is under threat: the ability to devise new molecules through R&D and bring them to market is not keeping up with what ’s being lost to generic manufacturers on the other end.
  • Anticancer Activity of Bicalutamide-Loaded PLGA Nanoparticles in Prostate Cancers

    Anticancer Activity of Bicalutamide-Loaded PLGA Nanoparticles in Prostate Cancers

    EXPERIMENTAL AND THERAPEUTIC MEDICINE 10: 2305-2310, 2015 Anticancer activity of bicalutamide-loaded PLGA nanoparticles in prostate cancers JUN GUO1*, SHU-HONG WU2*, WEI-GUO REN3, XIN-LI WANG4 and AI-QING YANG5 1Department of Urology, The Affiliated Hospital of Taishan Medical University, Taian, Shandong 271000; 2Department of Urology, Feicheng Kuangye Central Hospital, Feicheng, Shandong 271608; 3Department of Urology, Rugao Boai Hospital, Nantong, Jiangsu 226500; 4Department of Pathology, The Affiliated Hospital of Taishan Medical University;5 Department of Pathology, The First People's Hospital of Taian, Taian, Shandong 271000, P.R. China Received October 6, 2014; Accepted August 26, 2015 DOI: 10.3892/etm.2015.2796 Abstract. Prostate cancer is the most commonly diagnosed Introduction non-cutaneous malignancy in men in western and most devel- oping countries. Bicalutamide (BLT) is an antineoplastic Men in Western countries and developing nations are hormonal agent primarily used in the treatment of locally frequently diagnosed with cancers of the prostate gland (1). advanced and metastatic prostate cancers. In the present study, Statistically, prostate cancer has overtaken heart diseases and the aim was to develop a nanotechnology-based delivery lung disorders in old people as a cause of mortality. According system to target prostate cancer cells. This involved the to estimates, >200,000 new cases of prostate cancer were development of a BLT-loaded poly(D,L-lactide-co-glycolide) reported with >30,000 mortalities during the year 2013 (2,3). PLGA (PLGA-BLT) nanoparticulate system in an attempt to A similar or higher number of cases has been projected for the improve the therapeutic efficacy of BLT in prostate cancer and year 2014 (4).
  • Raloxifene for the Treatment of Triple Negative Breast Cancer

    Raloxifene for the Treatment of Triple Negative Breast Cancer

    Raloxifene for the Treatment of Triple Negative Breast Cancer Julian Dzeyk A Thesis Submitted for the Degree of Master of Science At the University of Otago, Dunedin, New Zealand Submitted on February 29th 2012 Acknowledgments I would like to sincerely thank Associate Professor Rhonda Rosengren for giving me the opportunity to work on this project as well as her help and guidance throughout the entire time. I want to greatly thank Dr Sebastien Taurin for teaching me the majority of techniques used in this study, as well as for always being there to discuss my endless questions and thoughts. I have learned a lot from you. Thank you for your continuous help. I would like to thank Babasaheb Yadav for his help and for always bringing a smile or laugh into the lab. You made the ECCO 2011 conference in Stockholm a trip to remember! Thank you. I would also like to thank Mhairi Nimick for her help around the lab, especially for tumor slicing when I had my back problems. To everyone else in the department and especially my fellow MSc students, thank you for the help and the good times. I would also like to thank my family in Germany who have always supported me in my plans and future goals and have always provided me with constructive feedback and ideas. To my dearest Steffi, thank you for your love, and your honest and continuous support. You have been a relentless source of motivation and inspiration. To Andrea and Bernard, thank you for your continuous support, your patience, your constructive criticism, your help, and most of all your love.
  • An Emerging Strategy for Rapid Target and Drug Discovery

    An Emerging Strategy for Rapid Target and Drug Discovery

    R E V I E W S CHEMOGENOMICS: AN EMERGING STRATEGY FOR RAPID TARGET AND DRUG DISCOVERY Markus Bredel*‡ and Edgar Jacoby§ Chemogenomics is an emerging discipline that combines the latest tools of genomics and chemistry and applies them to target and drug discovery. Its strength lies in eliminating the bottleneck that currently occurs in target identification by measuring the broad, conditional effects of chemical libraries on whole biological systems or by screening large chemical libraries quickly and efficiently against selected targets. The hope is that chemogenomics will concurrently identify and validate therapeutic targets and detect drug candidates to rapidly and effectively generate new treatments for many human diseases. Over the past five decades, pharmacological compounds however, that owing to the emergence of various sub- TRANSCRIPTIONAL PROFILING The study of the transcriptome have been identified that collectively target the products specialties of chemogenomics (discussed in the next — the complete set of RNA of ~400–500 genes in the human body; however, only section) and the involvement of several disciplines, it is transcripts that are produced by ~120 of these genes have reached the market as the tar- currently almost impossible to give a simple and com- the genome at any one time — gets of drugs1,2. The Human Genome Project3,4 has mon definition for this research discipline (BOX 1). using high-throughput methods, such as microarray made available many potential new targets for drug In chemogenomics-based drug discovery, large col- analysis. intervention: several thousand of the approximately lections of chemical products are screened for the paral- 30,000–40,000 estimated human genes4 could be associ- lel identification of biological targets and biologically ated with disease and, similarly, several thousand active compounds.