QSAR Modeling

Total Page:16

File Type:pdf, Size:1020Kb

QSAR Modeling Best practices for developing predictive QSAR models Alexander Tropsha Laboratory for Molecular Modeling and Carolina Center for Exploratory Cheminformatics Research School of Pharmacy UNC-Chapel Hill OUTLINE • Introduction: Brief outline of the QSAR approach • Why models fail (bad practices) • Good practices. – Predictive QSAR Modeling Workflow – Examples of the Workflow applications – Emerging applications of QSAR: chemocentric informatics • Conclusions: QSAR modeling is a decision support The rumors of QSAR demise have been greatly exaggerated 100,000 4500 90,000 4000 number of QSAR 80,000 Number of compounds papers in PubMed 3500 70,000 in CAS (in 1000s) 3000 60,000 2500 50,000 2000 40,000 Number of protein 1500 Structures in PDB 30,000 1000 20,000 500 10,000 0 0 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Graphs are courtesy of Prof. A. Cherkasov Principles of QSAR modeling Introduction O N C 0.613 O O N 0.380 A -0.222 O D C M N O E 0.708 N S O Quantitative 1.146 T P N C O R 0.491 N Structure I O I 0.301 O P Activity N 0.141 V U O T Relationships N O 0.956 I N O R N 0.256 S D 0.799 T O 1.195 N Y S 1.005 Principles of QSAR/QSPR modeling Introduction O N C 0.613 O O N 0.380 P -0.222 O D R M N O E 0.708 N S O Quantitative 1.146 O P N C O R 0.491 N Structure P O I 0.301 O P Property N 0.141 E U O T Relationships N O 0.956 R N O R N 0.256 S D 0.799 T O 1.195 N Y S 1.005 The utility of QSAR models CHEMICAL CHEMICAL PREDICTIVE PROPERTY/ STRUCTURES DESCRIPTORS QSAR MODELS ACTIVITY CHEMICAL DATABASE VIRTUAL HITS SCREENING ~106 – 109 molecules INACTIVES QSAR Modeling appears easy… Goal: Establish correlations between descriptors and the target property capable of predicting activities of novel compounds Chemistry Biology Cheminformatics (IC50, Kd...) (Molecular Descriptors) Comp.1 Value1 D1 D2 D3 | Dn Comp.2 Value2 " " " | " Comp.3 Value3 " " " | " - - - - - - - - - - - - - - - - - - - - - - Comp.N ValueN " " " | " ∑(y − yˆ )2 2 = − i i q 1 2 ∑ (y − yi ) BA = F(D) {e.g., …} (e.g., -LogIC50 = k1D1+k2D2+…+knDn) But … the unbearable lightness of model building for training sets… 3 2.5 2 Training 1.5 Linear (Training) 1 Predicted LogED50 0.5 0 0 1 2 3 4 Actual LogED50 (ED50 = mM/kg) …leads to unacceptable prediction accuracy. EXTERNAL TEST SET PREDICTIONS 9 y = 0.5958x + 2.3074 9 y = 0.4694x + 2.9313 2 R = 0.2135 R2 = 0.1181 7 7 5 Observed 5 Observed 3 3 3 5 7 9 3 5 7 9 Predicted Predicted BEWARE OF q2 (Kubinyi paradox)!!! 1 0.8 0.6 0.4 0.2 R2 0 0.5 0.6 0.7 0.8 0.9 1 -0.2 -0.4 -0.6 Poor Models Good Models -0.8 q2 •Only a small fraction of “predictive” training set models with LOO q2 > 0.6 is capable of making accurate predictions (r2 > 0.6) for the test sets. Golbraikh & Tropsha, J. Mol. Graphics Mod. 2002, 20, 269-276. Major components of QSAR modeling QSAR • Target properties (dependent variable) Pill – Continuous (e.g., IC50) – Categorical unrelated (e.g., different pharmacological classes) – Categorical related (e.g., subranges described as classes) • Descriptors (or independent variables) – Continuous (allows distance based similarity) – Categorical related (allows distance based similarity) – Categorical unrelated (require special similarity metrics) • Correlation methods (with and w/o variable selection) – Linear (e.g., LR, MLR, PCR, PLS) – Non-linear (e.g., kNN, RP, ANN, SVM) • Validation and prediction – Internal (training set) vs. external (test set) vs. independent evaluation set • Examples of applications and pitfalls Complexity of QSAR modeling: Choices and Practices • Descriptors (thousands and counting) • Data-analytical methods (dozens and counting) • Validation approaches (unfortunately (!) only a handful but counting) • Experimental validation as part of model building (very rare) BUT • We typically use one (or at best very few) modeling techniques • Publish successes only • Compete but (mostly) indirectly Why models may fail • Incorrect data (structures and activities) in the dataset • Modeling set is too small • No external validation • Incorrect selection of an external test set • Incorrect division of a dataset into training and test sets • Incorrect measure of prediction accuracy • Insufficient statistical criteria to estimate predictive power of models • Lack or incorrect definition of applicability domain • No Y-randomization test (overfitness) • Presence of leverage (structure) and activity outliers Also, see Dearden JC, Cronin MT, Kaiser KL. How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res. 2009;20(3-4):241-66 Some reasons why QSAR models may fail: using incorrect target function in classification QSAR for biased datasets: QSAR • A typical target function (Classification Rate): Pill CR=N(classified correctly)/N(total) A dataset: Class 1: 80 compounds Class 2: 20 compounds Model: assign all compounds to Class 1. Target function: CR=0.8 The model appears to have high classification accuracy • Better target function: K – the number of classes CCR=0.5(Sensitivity+Specificity) corr In the above example, CCR = 0.5 Nk – the number of compounds of class k assigned to class k • General formula: corr total 1 K N Nk – total number of compounds = k of class k CCR ∑ total K k =1 Nk • For categorical response variable, target functions can depend also on the absolute errors (differences between predicted and observed classes). HOW TO DEFINE A PREDICTIVE QSAR MODEL 9 10 10 y = 0.3154x + 3.4908 y = 3.1007x - 10.715 2 y = 1.2458x - 1.8812 R = 0.9778 R 2 = 0.9778 8 8 R 2 = 0.8604 7 6 6 4 4 Predicted Observed Observed 5 y = 1.0023x y = 0.9383x 2 2 y = 0.9796x 2 2 R 0 = 0.5238 2 R 0 = -3.3825 R 0 = 0.8209 0 0 3 0 2 4 6 8 10 0 2 4 6 8 10 3 5 7 9 Predicted Observed Predicted Regression through Regression Correlation coefficient Coefficients of determination the origin ~ r y = a' y + b' ( y − y)(~y − ~y) ~ r0 2 ∑ i i r0 − R = ~y = k' y 2 ∑ ( yi yi ) 2 ~ ~ 2 R = 1 − ~ ~ ∑ ( yi − y) ∑( yi − y) 0 2 ∑ (yi − y)(yi − y) ~ ~ a'= ∑ ( yi − y) 2 ~ (y−− y)~ − ~ ∑ yi yi ∑∑( yi i y)( yi y) = k' 2 r0 2 a = 2 ~ ~ − ~ yi ( yi − yi ) ~∑ ( yi y) ∑ 2 ∑ b'= y − a' y R'0 = 1− − 2 ~ ∑ ( yi y) b = y − ay CRITERIA y r0 = k~y 2 > 2 > y ~y q 0.5; R 0.6; k = ∑ i i ~ 2 ∑ yi ' 2 '2 2 k or k ≈ 1.0; R0 or R0 ≈ R Some reasons why QSAR models may fail: No Applicability Domain is defined for the Model • Compounds which are highly dissimilar from all compounds of the training set (according to the set of descriptors selected) cannot be predicted reliably Lack of the AD: QSAR Pill unjustified extrapolation wrong prediction Typical situation: a compound of the test set for which error of prediction is high is considered an outlier HOWEVER: a compound of the test set dissimilar from all compounds of the training set can be by chance predicted accurately Applicability domain of QSAR models For a given model, two parameters are calculated: Descriptor 2 - <Dk> : average Euclidian distance between each compound of the training set and its k nearest neighbors in the descriptors space; - sk : standard deviation of the distances between each compound of the training set and its k nearest neighbors in the descriptors space. TRAINING SET Descriptor 1 Atelier Descripteurs 200 Applicability domain of QSAR models For a given model, two parameters are calculated: Descriptor 2 - <Dk> : average euclidian distance between each compound of the training set and its k nearest neighbors in the descriptors space; - sk : standard deviation of the distances between each compound of the training set and its k nearest neighbors in the descriptors space. TRAINING SET Descriptor 1 = NEW COMPOUND For each test compound i, the distance Di is calculated as the average of the distances between i and its k nearest neighbors in the training set. INSIDE THE DOMAIN OUTSIDE THE DOMAIN The new compound will be predicted by the model, only if : Will be predicted Will not be predicted Di ≤ <Dk> + Z × sk by the model by the model with Z, an empirical parameter (0.5 by default) *Tropsha, A., Gramatica, P., Gombar, V. The importance of being earnest:… Quant. Struct. Act. Relat. Comb. Sci. 2003, 22, 69-77. Atelier Descripteurs 200 Applicability domain vs. prediction accuracy (Ames Genotoxicity dataset) 92 90 88 86 84 Test Set %Accuracy Set Test 82 80 0.5 1 2 3 5 10 kNN Z-Score Used for Prediction Cutoff Some reasons why QSAR models may fail: Y-randomization test is not QSAR carried out Pill • Y-randomization test: – Scramble activities of the training set – Build models and get model statistics. – If statistics are comparable to those obtained for models built with real activities of the training set, the last are unreliable and should be discarded. Frequently, Y-randomization test is not carried out. Y-randomization test is of particular importance, if there is: - a small number of compounds in the training or test set - response variable is categorical Activity randomization: model robustness Struc.1 Pro.1 Struc.2 Pro.2 0.7 Struc.3 Pro.3 0.6 2 .
Recommended publications
  • Clomifene Citrate(BANM, Rinnm) ⊗
    2086 Sex Hormones and their Modulators Profasi; UK: Choragon; Ovitrelle; Pregnyl; USA: Chorex†; Choron; Gonic; who received the drug for a shorter period.6 No association be- 8. Werler MM, et al. Ovulation induction and risk of neural tube Novarel; Ovidrel; Pregnyl; Profasi; Venez.: Ovidrel; Pregnyl; Profasi†. tween gonadotrophin therapy and ovarian cancer was noted in defects. Lancet 1994; 344: 445–6. Multi-ingredient: Ger.: NeyNormin N (Revitorgan-Dilutionen N Nr this study. The conclusions of this study were only tentative, 9. Greenland S, Ackerman DL. Clomiphene citrate and neural tube 65)†; Mex.: Gonakor. defects: a pooled analysis of controlled epidemiologic studies since the numbers who developed ovarian cancer were small; it and recommendations for future studies. Fertil Steril 1995; 64: has been pointed out that a successfully achieved pregnancy may 936–41. reduce the risk of some other cancers, and that the risks and ben- 10. Whiteman D, et al. Reproductive factors, subfertility, and risk efits of the procedure are not easy to balance.7 A review8 of epi- of neural tube defects: a case-control study based on the Oxford Clomifene Citrate (BANM, rINNM) ⊗ Record Linkage Study Register. Am J Epidemiol 2000; 152: demiological and cohort studies concluded that clomifene was 823–8. Chloramiphene Citrate; Citrato de clomifeno; Clomifène, citrate not associated with any increase in the risk of ovarian cancer 11. Sørensen HT, et al. Use of clomifene during early pregnancy de; Clomifeni citras; Clomiphene Citrate (USAN); Klomifeenisi- when used for less than 12 cycles, but noted conflicting results, and risk of hypospadias: population based case-control study.
    [Show full text]
  • (12) Patent Application Publication (10) Pub. No.: US 2006/0110428A1 De Juan Et Al
    US 200601 10428A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2006/0110428A1 de Juan et al. (43) Pub. Date: May 25, 2006 (54) METHODS AND DEVICES FOR THE Publication Classification TREATMENT OF OCULAR CONDITIONS (51) Int. Cl. (76) Inventors: Eugene de Juan, LaCanada, CA (US); A6F 2/00 (2006.01) Signe E. Varner, Los Angeles, CA (52) U.S. Cl. .............................................................. 424/427 (US); Laurie R. Lawin, New Brighton, MN (US) (57) ABSTRACT Correspondence Address: Featured is a method for instilling one or more bioactive SCOTT PRIBNOW agents into ocular tissue within an eye of a patient for the Kagan Binder, PLLC treatment of an ocular condition, the method comprising Suite 200 concurrently using at least two of the following bioactive 221 Main Street North agent delivery methods (A)-(C): Stillwater, MN 55082 (US) (A) implanting a Sustained release delivery device com (21) Appl. No.: 11/175,850 prising one or more bioactive agents in a posterior region of the eye so that it delivers the one or more (22) Filed: Jul. 5, 2005 bioactive agents into the vitreous humor of the eye; (B) instilling (e.g., injecting or implanting) one or more Related U.S. Application Data bioactive agents Subretinally; and (60) Provisional application No. 60/585,236, filed on Jul. (C) instilling (e.g., injecting or delivering by ocular ion 2, 2004. Provisional application No. 60/669,701, filed tophoresis) one or more bioactive agents into the Vit on Apr. 8, 2005. reous humor of the eye. Patent Application Publication May 25, 2006 Sheet 1 of 22 US 2006/0110428A1 R 2 2 C.6 Fig.
    [Show full text]
  • Customs Tariff - Schedule
    CUSTOMS TARIFF - SCHEDULE 99 - i Chapter 99 SPECIAL CLASSIFICATION PROVISIONS - COMMERCIAL Notes. 1. The provisions of this Chapter are not subject to the rule of specificity in General Interpretative Rule 3 (a). 2. Goods which may be classified under the provisions of Chapter 99, if also eligible for classification under the provisions of Chapter 98, shall be classified in Chapter 98. 3. Goods may be classified under a tariff item in this Chapter and be entitled to the Most-Favoured-Nation Tariff or a preferential tariff rate of customs duty under this Chapter that applies to those goods according to the tariff treatment applicable to their country of origin only after classification under a tariff item in Chapters 1 to 97 has been determined and the conditions of any Chapter 99 provision and any applicable regulations or orders in relation thereto have been met. 4. The words and expressions used in this Chapter have the same meaning as in Chapters 1 to 97. Issued January 1, 2020 99 - 1 CUSTOMS TARIFF - SCHEDULE Tariff Unit of MFN Applicable SS Description of Goods Item Meas. Tariff Preferential Tariffs 9901.00.00 Articles and materials for use in the manufacture or repair of the Free CCCT, LDCT, GPT, UST, following to be employed in commercial fishing or the commercial MT, MUST, CIAT, CT, harvesting of marine plants: CRT, IT, NT, SLT, PT, COLT, JT, PAT, HNT, Artificial bait; KRT, CEUT, UAT, CPTPT: Free Carapace measures; Cordage, fishing lines (including marlines), rope and twine, of a circumference not exceeding 38 mm; Devices for keeping nets open; Fish hooks; Fishing nets and netting; Jiggers; Line floats; Lobster traps; Lures; Marker buoys of any material excluding wood; Net floats; Scallop drag nets; Spat collectors and collector holders; Swivels.
    [Show full text]
  • )&F1y3x PHARMACEUTICAL APPENDIX to THE
    )&f1y3X PHARMACEUTICAL APPENDIX TO THE HARMONIZED TARIFF SCHEDULE )&f1y3X PHARMACEUTICAL APPENDIX TO THE TARIFF SCHEDULE 3 Table 1. This table enumerates products described by International Non-proprietary Names (INN) which shall be entered free of duty under general note 13 to the tariff schedule. The Chemical Abstracts Service (CAS) registry numbers also set forth in this table are included to assist in the identification of the products concerned. For purposes of the tariff schedule, any references to a product enumerated in this table includes such product by whatever name known. Product CAS No. Product CAS No. ABAMECTIN 65195-55-3 ACTODIGIN 36983-69-4 ABANOQUIL 90402-40-7 ADAFENOXATE 82168-26-1 ABCIXIMAB 143653-53-6 ADAMEXINE 54785-02-3 ABECARNIL 111841-85-1 ADAPALENE 106685-40-9 ABITESARTAN 137882-98-5 ADAPROLOL 101479-70-3 ABLUKAST 96566-25-5 ADATANSERIN 127266-56-2 ABUNIDAZOLE 91017-58-2 ADEFOVIR 106941-25-7 ACADESINE 2627-69-2 ADELMIDROL 1675-66-7 ACAMPROSATE 77337-76-9 ADEMETIONINE 17176-17-9 ACAPRAZINE 55485-20-6 ADENOSINE PHOSPHATE 61-19-8 ACARBOSE 56180-94-0 ADIBENDAN 100510-33-6 ACEBROCHOL 514-50-1 ADICILLIN 525-94-0 ACEBURIC ACID 26976-72-7 ADIMOLOL 78459-19-5 ACEBUTOLOL 37517-30-9 ADINAZOLAM 37115-32-5 ACECAINIDE 32795-44-1 ADIPHENINE 64-95-9 ACECARBROMAL 77-66-7 ADIPIODONE 606-17-7 ACECLIDINE 827-61-2 ADITEREN 56066-19-4 ACECLOFENAC 89796-99-6 ADITOPRIM 56066-63-8 ACEDAPSONE 77-46-3 ADOSOPINE 88124-26-9 ACEDIASULFONE SODIUM 127-60-6 ADOZELESIN 110314-48-2 ACEDOBEN 556-08-1 ADRAFINIL 63547-13-7 ACEFLURANOL 80595-73-9 ADRENALONE
    [Show full text]
  • Index to the NLM Classification 2011
    National Library of Medicine Classification 2011 Index Disease see Tyrosinemias 1-8 5,12-diHETE see Leukotriene B4 1,2-Benzopyrones see Coumarins 5,12-HETE see Leukotriene B4 1,2-Dibromoethane see Ethylene Dibromide 5-HT see Serotonin 1,8-Dihydroxy-9-anthrone see Anthralin 5-HT Antagonists see Serotonin Antagonists 1-Oxacephalosporin see Moxalactam 5-Hydroxytryptamine see Serotonin 1-Propanol 5-Hydroxytryptamine Antagonists see Serotonin Organic chemistry QD 305.A4 Antagonists Pharmacology QV 82 6-Mercaptopurine QV 269 1-Sar-8-Ala Angiotensin II see Saralasin 7S RNA see RNA, Small Nuclear 1-Sarcosine-8-Alanine Angiotensin II see Saralasin 8-Hydroxyquinoline see Oxyquinoline 13-cis-Retinoic Acid see Isotretinoin 8-Methoxypsoralen see Methoxsalen 15th Century History see History, 15th Century 8-Quinolinol see Oxyquinoline 16th Century History see History, 16th Century 17 beta-Estradiol see Estradiol 17-Ketosteroids WK 755 A 17-Oxosteroids see 17-Ketosteroids A Fibers see Nerve Fibers, Myelinated 17th Century History see History, 17th Century Aardvarks see Xenarthra 18th Century History see History, 18th Century Abate see Temefos 19th Century History see History, 19th Century Abattoirs WA 707 2',3'-Cyclic-Nucleotide Phosphodiesterases QU 136 Abbreviations 2,4-D see 2,4-Dichlorophenoxyacetic Acid Chemistry QD 7 2,4-Dichlorophenoxyacetic Acid General P 365-365.5 Organic chemistry QD 341.A2 Library symbols (U.S.) Z 881 2',5'-Oligoadenylate Polymerase see Medical W 13 2',5'-Oligoadenylate Synthetase By specialties (Form number 13 in any NLM
    [Show full text]
  • Electrochemical Behaviour and Quantitative Determination Of
    Int J Pharma Res Health Sci. 2017; 5 (6): 2014-18 DOI:10.21276/ijprhs.2017.06.22 Reddy et al CODEN (USA)-IJPRUR, e-ISSN: 2348-6465 International Journal of Pharma Research and Health Sciences Available online at www.pharmahealthsciences.net Original Article Electrochemical Behaviour and Quantitative Determination of Clomifene in Pharmaceutical Formulations C Nageswara Reddy1,*, P Sandhya 3, P R Prasad 2, N Y Sreedhar 2 1Department of Chemistry, Govt. Degree & P.G. College, Puttur-517 583, A.P., India. 2Electroanalytical Lab, Department of Chemistry, Sri Venkateswara University, Tirupati-517 502, A.P, India. 3Department of Chemistry, Sri Padmavati Mahila Visvavidyalayam, Tirupati-517 502, A.P, India. ARTICLE INFO ABSTRACT Received: 11 Nov 2017 The electrochemical behaviour of clomifene was studied and determined in pharmaceutical Accepted: 18 Dec 2017 formulations. The drug of clomifene was electrochemically examined by cyclic and differential pulse voltammetry using a glassy carbon electrode (GCE). The obtained results showed that, the Britton Robinson buffer with pH 4.0 was the best medium for reduction of clomifene on the glassy carbon electrode at the peak potential -1.01 V. The range of linearity was found to be from 0.20 μg mL-1 to 60.0 μg mL-1 (R2 = 0.994) with limit of detection (LOD) 0.168 μg mL-1 and limit of quantification (LOQ) was 0.511 μg mL-1. Differential pulse voltammetric method was successfully applied for the electrochemical determination of clomifene in pharmaceutical formulations ______ Keywords: Clomifene, drug, voltammetry, pharmaceutical formulations. 1. INTRODUCTION An alkenyl group containing molecules play a vital role in drug chemistry.1 There are innumerable chemical reactions that involve or produce a carbon-carbon double bond group as an intermediate or as an end produce.1 Consequently the discovery of the first non-steroidal antiestrogen, several studies have been conducted to determine the mechanism of Corresponding author * action of this classes of compounds.
    [Show full text]
  • The Use of Stems in the Selection of International Nonproprietary Names (INN) for Pharmaceutical Substances
    WHO/PSM/QSM/2006.3 The use of stems in the selection of International Nonproprietary Names (INN) for pharmaceutical substances 2006 Programme on International Nonproprietary Names (INN) Quality Assurance and Safety: Medicines Medicines Policy and Standards The use of stems in the selection of International Nonproprietary Names (INN) for pharmaceutical substances FORMER DOCUMENT NUMBER: WHO/PHARM S/NOM 15 © World Health Organization 2006 All rights reserved. Publications of the World Health Organization can be obtained from WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 3264; fax: +41 22 791 4857; e-mail: [email protected]). Requests for permission to reproduce or translate WHO publications – whether for sale or for noncommercial distribution – should be addressed to WHO Press, at the above address (fax: +41 22 791 4806; e-mail: [email protected]). The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.
    [Show full text]
  • Patent Application Publication ( 10 ) Pub . No . : US 2019 / 0192440 A1
    US 20190192440A1 (19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2019 /0192440 A1 LI (43 ) Pub . Date : Jun . 27 , 2019 ( 54 ) ORAL DRUG DOSAGE FORM COMPRISING Publication Classification DRUG IN THE FORM OF NANOPARTICLES (51 ) Int . CI. A61K 9 / 20 (2006 .01 ) ( 71 ) Applicant: Triastek , Inc. , Nanjing ( CN ) A61K 9 /00 ( 2006 . 01) A61K 31/ 192 ( 2006 .01 ) (72 ) Inventor : Xiaoling LI , Dublin , CA (US ) A61K 9 / 24 ( 2006 .01 ) ( 52 ) U . S . CI. ( 21 ) Appl. No. : 16 /289 ,499 CPC . .. .. A61K 9 /2031 (2013 . 01 ) ; A61K 9 /0065 ( 22 ) Filed : Feb . 28 , 2019 (2013 .01 ) ; A61K 9 / 209 ( 2013 .01 ) ; A61K 9 /2027 ( 2013 .01 ) ; A61K 31/ 192 ( 2013. 01 ) ; Related U . S . Application Data A61K 9 /2072 ( 2013 .01 ) (63 ) Continuation of application No. 16 /028 ,305 , filed on Jul. 5 , 2018 , now Pat . No . 10 , 258 ,575 , which is a (57 ) ABSTRACT continuation of application No . 15 / 173 ,596 , filed on The present disclosure provides a stable solid pharmaceuti Jun . 3 , 2016 . cal dosage form for oral administration . The dosage form (60 ) Provisional application No . 62 /313 ,092 , filed on Mar. includes a substrate that forms at least one compartment and 24 , 2016 , provisional application No . 62 / 296 , 087 , a drug content loaded into the compartment. The dosage filed on Feb . 17 , 2016 , provisional application No . form is so designed that the active pharmaceutical ingredient 62 / 170, 645 , filed on Jun . 3 , 2015 . of the drug content is released in a controlled manner. Patent Application Publication Jun . 27 , 2019 Sheet 1 of 20 US 2019 /0192440 A1 FIG .
    [Show full text]
  • Marrakesh Agreement Establishing the World Trade Organization
    No. 31874 Multilateral Marrakesh Agreement establishing the World Trade Organ ization (with final act, annexes and protocol). Concluded at Marrakesh on 15 April 1994 Authentic texts: English, French and Spanish. Registered by the Director-General of the World Trade Organization, acting on behalf of the Parties, on 1 June 1995. Multilat ral Accord de Marrakech instituant l©Organisation mondiale du commerce (avec acte final, annexes et protocole). Conclu Marrakech le 15 avril 1994 Textes authentiques : anglais, français et espagnol. Enregistré par le Directeur général de l'Organisation mondiale du com merce, agissant au nom des Parties, le 1er juin 1995. Vol. 1867, 1-31874 4_________United Nations — Treaty Series • Nations Unies — Recueil des Traités 1995 Table of contents Table des matières Indice [Volume 1867] FINAL ACT EMBODYING THE RESULTS OF THE URUGUAY ROUND OF MULTILATERAL TRADE NEGOTIATIONS ACTE FINAL REPRENANT LES RESULTATS DES NEGOCIATIONS COMMERCIALES MULTILATERALES DU CYCLE D©URUGUAY ACTA FINAL EN QUE SE INCORPOR N LOS RESULTADOS DE LA RONDA URUGUAY DE NEGOCIACIONES COMERCIALES MULTILATERALES SIGNATURES - SIGNATURES - FIRMAS MINISTERIAL DECISIONS, DECLARATIONS AND UNDERSTANDING DECISIONS, DECLARATIONS ET MEMORANDUM D©ACCORD MINISTERIELS DECISIONES, DECLARACIONES Y ENTEND MIENTO MINISTERIALES MARRAKESH AGREEMENT ESTABLISHING THE WORLD TRADE ORGANIZATION ACCORD DE MARRAKECH INSTITUANT L©ORGANISATION MONDIALE DU COMMERCE ACUERDO DE MARRAKECH POR EL QUE SE ESTABLECE LA ORGANIZACI N MUND1AL DEL COMERCIO ANNEX 1 ANNEXE 1 ANEXO 1 ANNEX
    [Show full text]
  • Assessment Report
    25 January 2018 EMA/88321/2018 Committee for Medicinal Products for Human Use (CHMP) Assessment report EnCyzix International non-proprietary name: enclomifene Procedure No. EMEA/H/C/004198/0000 Note Assessment report as adopted by the CHMP with all information of a commercially confidential nature deleted. 30 Churchill Place ● Canary Wharf ● London E14 5EU ● United Kingdom Telephone +44 (0)20 3660 6000 Facsimile +44 (0)20 3660 5555 Send a question via our website www.ema.europa.eu/contact An agency of the European Union © European Medicines Agency, 2018. Reproduction is authorised provided the source is acknowledged. Administrative information Name of the medicinal product: EnCyzix Applicant: Renable Pharma Limited 20-22 Bedford Row WC1R 4JS UNITED KINGDOM Active substance: ENCLOMIFENE CITRATE International Non-proprietary Name/Common Enclomifene Name: Not assigned Pharmaco-therapeutic group (ATC Code): Treatment of hypogonadotropic hypogonadism (secondary hypogonadism) in Therapeutic indication(s): adult men aged ≤60 years with a body mass index (BMI) ≥25 kg/m2 which has been confirmed by clinical features and biochemical tests in patients which have not responded to diet and exercise Pharmaceutical form(s): Capsule, hard Strength(s): 8.5 mg and 17 mg Route(s) of administration: Oral use Packaging: bottle (HDPE) Package size(s): 30 capsules EMA/88321/2018 Page 2/111 Table of contents 1. Background information on the procedure .............................................. 7 1.1. Submission of the dossier ....................................................................................
    [Show full text]
  • N-Alkylamino Derivates of Aromatic, Tricyclic Compounds in the Treatment of Drug-Resistant Protozoal Infections
    Europaisches Patentamt 0 338 532 European Patent Office Qv Publication number: A2 Office europeen des brevets EUROPEAN PATENT APPLICATION 31/54 © Application number: 89107027.8 © mt.ci.4.A61K 31/38 , A61K @ Date of filing: 19.04.89 © Priority: 20.04.88 US 183858 © Applicant: MERRELL DOW 12.09.88 US 243524 PHARMACEUTICALS INC. 2110 East Galbraith Road @. Date of publication of application: Cincinnati Ohio 45215-6300(US) 25.10.89 Bulletin 89/43 © Inventor: Bitonti, Alan J. © Designated Contracting States: 7854 Carraway Court AT BE CH DE ES FR GB GR IT LI LU NL SE Maineville Ohio 45039(US) Inventor: McCann, Peter P. 3782 Cooper Road Cincinnati Ohio 45241 (US) Inventor: Sjoerdsma, Albert 5475 Waring Drive Cincinnati Ohio 45243(US) © Representative: Vossius & Partner Siebertstrasse 4 P.O. Box 86 07 67 D-8000 Munchen 86(DE) © N-alkylamino derivates of aromatic, tricyclic compounds in the treatment of drug-resistant protozoal infections. © Drug-resistant protozoal infection, particularly, drug-resistant malarial infection in humans, can be effectively treated with standard antiprotozoal agents if administered in conjunction with a tricyclic N-aminoalkyi iminodiben- zyl, dibenzylcycloheptane and cycloheptene, phenothiazine, dibenzoxazepine, dibenzoxepine, or thioxanthene derivative. CM < CM CO in oo CO CO LU Xerox Copy Centre EP 0 338 532 A2 N-ALKYLAMINO DERIVATIVES OF AROMATIC, TRICYCLIC COMPOUNDS IN THE TREATMENT OF DRUG- RESISTANT PROTOZOAL INFECTIONS This invention relates to the use of certain N-(aminoalkyl) derivatives of iminodibenzyl, dibenzyl- cycioheptane and cycloheptene, phenothiazine, dibenzoxazepine, dibenzoxepine, and thioxanthene in the treatment of drug-resistant malaria and in the treatment of other drug-resistant protozoal infections.
    [Show full text]
  • Clomifene Pharmacokinetic Data Bioavailability High (>90
    Clomifene Pharmacokinetic data Bioavailability High (>90%) Metabolism Hepatic (with enterohepatic circulation) Half-life 5-7 days Excretion Mainly renal, some biliary Clomifene (INN) or clomiphene (USAN) (trademarked as Androxal, Clomid and Omifin) is a selective estrogen receptor modulator (SERM) that increases production of gonadotropins by inhibiting negative feedback on the hypothalamus. This synthetic drug comes supplied as white, round tablets in 50 mg strength only. It has become the most widely prescribed of all fertility drugs. It is used in the form of its citrate to induce ovulation. Use in female infertility It is used mainly in female infertility, in turn mainly as ovulation induction to reverse oligoovulation or anovulation such as in infertility in polycystic ovary syndrome, as well as being used for ovarian hyperstimulation, such as part of an in vitro fertilization procedure. In April 1989, a patent was awarded to Yale University Medical researchers Dr. Florence Comite and Dr. Pamela Jensen for the use of clomiphene to predict fertility in women. A woman capable of sustaining pregnancy would develop denser bone mass while on clomiphene; the bone mass increase would be predictive of fertility, and the changes could be detected on a CT scanner. Mode of action Clomifene inhibits estrogen receptors in hypothalamus, inhibiting negative feedback of estrogen on gonadotropin release, leading to up-regulation of the hypothalamic–pituitary–gonadal axis. Zuclomifene, a more active isomer, stays bound for longer periods of time. The drug is non-steroidal. In normal physiologic female hormonal cycling, at 7 days past ovulation, high levels of estrogen and progesterone produced from the corpus luteum inhibit GnRH, FSH and LH at the hypothalamus and anterior pituitary.
    [Show full text]