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Drug Repurposing in Healthcare Dr. Prerana.pptx
- 1. DRUG REPURPOSING Dr. Prerana Manik Kadam (JR III) Guide : Dr Smita Anand Tiwari Dr. Prerana Manik Kadam 1
- 2. CONTENTS ⮚Drug repurposing: Definition ⮚Introduction ⮚History ⮚Drug development process ⮚Traditional vs drug repurposing process ⮚Strategies of drug repurposing ⮚Approaches to drug repurposing a. Computational b. Experimental c. Mixed ⮚Success stories ⮚Failure stories ⮚Challenges ⮚Applications ⮚Indian scenario ⮚Conclusion Dr. Prerana Manik Kadam 2
- 3. DRUG REPURPOSING “ It is a process of identification of new pharmacological indications from old/existing/failed/investigational/already marketed/FDA approved drugs/pro- drugs and the application of the newly developed drugs to the treatment of diseases other than the drug’s original/intended therapeutic use.” Establish new therapeutic uses for :- Already known drugs Failed Investigational Already marketed FDA approved Prodrug Dr. Prerana Manik Kadam 3
- 4. Drug repurposing Drug re-tasking Drug rescuing Drug repositioning Drug re-profiling Drug recycling Drug redirection Therapeutic switching DRUG REPURPOSING Renewing FAILED drugs + Expanding OLD drugs Dr. Prerana Manik Kadam 4
- 5. INTRODUCTION Pharmaceutical industry- generates 25% of the annual revenue Pharmaceutical industries 🡪 market growth - $ 6 billion USD increase One-third of the new drug approvals are repurposed drugs 30% of the approved drugs and biologics (vaccines) Dr. Prerana Manik Kadam 5
- 6. HISTORY ▪ Accidental discovery/serendipitous observations ▪ Marcell Johnson prescribed Sulfonamides during the 2nd World War ▪ Adverse effect : Hypoglycemia, deaths due to hypoglycemic coma ▪ Development of Antidiabetic agents from Sulfonamides - Sulfonylureas Best-known drug of this approach Sildenafil Original indication - Angina pectoris New indication - Erectile dysfunction Dr. Prerana Manik Kadam 6
- 7. TRADITIONAL APPROACH (Five stages) Discovery and preclinical testing Safety review Clinical research FDA review FDA post-market safety monitoring DRUG REPURPOSING APPROACH (Four stages) Compound identification Compound acquisition Development FDA post-market safety monitoring DRUG DEVELOPMENT PROCESS Dr. Prerana Manik Kadam 7
- 8. DRUG DEVELOPMENT PROCESS Dr. Prerana Manik Kadam 8
- 9. TRADITIONAL VS DRUG REPURPOSING PROCESS TRADITIONAL DRUG REPURPOSING Unknown safety Known safety Time-consuming (10-17 years) Less time than traditional (6-10 years) Costly 1.24 billion USD Less expensive <60% High risk of failure Drug present in market even after failure Dr. Prerana Manik Kadam 9
- 10. STRATEGIES Drug based Target based Disease based Dr. Prerana Manik Kadam 10
- 11. DRUG - BASED R1 R1 and R2 have similar profile (structural characteristics, biological activities, adverse effects and toxicities) R1 can treat disease D, then R2 can also treat disease D OLANZAPINE ONDANSETRON CINV D R2 Dr. Prerana Manik Kadam 11
- 12. TARGET- BASED • In silico screening or Virtual high-through-put screening (vHTS) of drugs or compounds Drug libraries Compound databases Selective protein molecule or a biomarker of interest • Significant success rate - most biological targets directly represent the disease pathways/mechanisms Dr. Prerana Manik Kadam 12
- 13. ON-TARGET STRATEGY Dr. Prerana Manik Kadam 13
- 14. EXAMPLE MINOXIDIL K+ channel opener Vasodilator Oral Anti hypertensive Topical Androgenic alopecia Dr. Prerana Manik Kadam 14
- 15. OFF-TARGET STRATEGY Dr. Prerana Manik Kadam 15
- 16. EXAMPLE ASPIRIN COX Inhibitor Rx of pain and inflammatory disorders Antiplatelet action Rx of MI and stroke Dr. Prerana Manik Kadam 16
- 17. D1 Disease D1 and D2 have the same phenotype A drug R that can treat disease D1, has the property to treat disease D2 D2 PULMONARY HYPERTENSION SILDENAFIL R DISEASE/THERAPY BASED ERECTILE DYSFUNCTION Dr. Prerana Manik Kadam 17
- 18. APPROACHES TO DRUG REPURPOSING Dr. Prerana Manik Kadam 18
- 19. MIXED EXPERIMENTA L COMPUTATIONAL In silico Phenotype TYPES OF APPROACHES a) Profile b) Network c) Database Expression Chemical Clinical In vitro In vivo Clinical Dr. Prerana Manik Kadam 19
- 20. COMPUTATIONAL/ IN SILICO APPROACH By using computational tools, virtually screening public databases (drug/chemical) libraries Uses computational biology and bioinformatics/chemo- informatics tools Identification of potential bioactive molecules - based upon the molecular interaction between drug and target Formulation of repurposing hypotheses Dr. Prerana Manik Kadam 20
- 21. Publicly accessible Target databases • Drug bank • Potential Drug Target Database • Therapeutic Target Database • Super Target Ligand knowledge bases • World Drug Index • MDI Drug data report • WOMBAT • AurSCOPE • ChemBioBase • GVKBio Online sources • Prous Investigational drug database • Adis Insight Trial trove • Information from Patents, conferences, website etc. Electronic ORANGE BOOK • US FDA • DISC – Discontinued drug products • Phase I – withdrawn for reasons other than safety DATA SOURCES Information about therapeutic targets After identifying therapeutic targets Dr. Prerana Manik Kadam 21
- 22. John Hopkins clinical compound screening initiative - 2002 - 3000 drugs National Institute of Health (NIH) - 450 small molecules - History of use in human clinical trials NIH Chemical Genomics Center With NIH Pharmaceutical Collection (NPC) - Drugs for human trials Novel public private partnership initiative - Access to Pfizer indications discovery unit COMPOUND LIBRARIES Dr. Prerana Manik Kadam 22
- 23. TYPES OF IN SILICO APPROACHES Dr. Prerana Manik Kadam 23
- 24. MIXED EXPERIMENTA L COMPUTATIONAL In silico Phenotype TYPES OF APPROACHES a) Profile b) Network c) Database Expression Chemical Clinical In vitro In vivo Clinical Dr. Prerana Manik Kadam 24
- 25. PROFILE BASED (SIGNATURE MATCHING) Expression profile • Transcriptomic profile Chemical structure profile • Molecular docking Clinical profile • Side effects profile Dr. Prerana Manik Kadam 25
- 26. EXPRESSION PROFILE • A strong negative correlation between transcriptomic profile of drug and disease will suggest potential effect of drug on disease Disease Drug • Analysis of transcriptomic profile between drug and disease Dr. Prerana Manik Kadam 26
- 27. Dr. Prerana Manik Kadam 27
- 28. CHEMICAL STRUCTURE BASED MOLECULAR DOCKING • Structure based computational strategy • To predict binding site complimentary between ligand and target • Two types Conventional Inverse Dr. Prerana Manik Kadam 28
- 29. CONVENTIONAL DOCKING One Target, Several ligands Dr. Prerana Manik Kadam 29
- 30. Several targets, One ligand INVERSE DOCKING Dr. Prerana Manik Kadam 30
- 31. CLINICAL PROFILE - BASED Two drugs cause same adverse effect Suggests that both may share a target protein or pathway Adverse effect of particular drug may resemble a disease Suggests shared pathways and physiology by both drug and disease Dr. Prerana Manik Kadam 31
- 32. DRUG - DRUG Dr. Prerana Manik Kadam 32
- 33. DRUG – DISEASE Dr. Prerana Manik Kadam 33
- 34. MIXED EXPERIMENTA L COMPUTATIONAL In silico Phenotype TYPES OF APPROACHES a) Profile b) Network c) Database Expression Chemical Clinical In vitro In vivo Clinical Dr. Prerana Manik Kadam 34
- 35. NETWORK BASED PATHWAY Integrates multiple data sources and combines information regarding drugs and their druggable targets to predict potential drug candidates Dr. Prerana Manik Kadam 35
- 36. NETWORK BASED PATHWAY Dr. Prerana Manik Kadam 36
- 37. Dr. Prerana Manik Kadam 37
- 38. http://genome.ugr.es:9000 DRUG.NET Dr. Prerana Manik Kadam 38
- 39. MIXED EXPERIMENTA L COMPUTATIONAL In silico Phenotype TYPES OF APPROACHES a) Profile b) Network c) Database Expression Chemical Clinical In vitro In vivo Clinical Dr. Prerana Manik Kadam 39
- 40. DATA BASED – DATA MINING Text mining is the discovery by computer of new, previously unknown information by automatically extracting information from different written sources Dr. Prerana Manik Kadam 40
- 41. ABC MODEL Dr. Prerana Manik Kadam 41
- 42. MIXED EXPERIMENTA L COMPUTATIONAL In silico Phenotype TYPES OF APPROACHES a) Profile b) Network c) Database Expression Chemical Clinical In vitro In vivo Clinical Dr. Prerana Manik Kadam 42
- 43. EXPERIMENT BASED APPROACH • Also known as Activity-based repositioning • Protein target-based and cell/organism-based screens in vitro and/or in vivo disease models • No requirements of any structural information of target proteins. • Examples: Target screening approach Cell assay approach Animal model approach Clinical approach Dr. Prerana Manik Kadam 43
- 44. EXPERIMENT BASED APPROACH TYPES Phenotype screening Clinical approaches Dr. Prerana Manik Kadam 44
- 45. Cell based assay Cancer cell lines(CCLs) Organism based assay Zebrafish In vitro techniques It can identify compounds that show disease relevant effects in animal model systems without prior knowledge target PHENOTYPE SCREENING In vivo techniques Dr. Prerana Manik Kadam 45
- 46. CLINICAL SCREENING Pre Marketing Post Marketing Retrospective Clinical Analysis Dr. Prerana Manik Kadam 46
- 47. OBSERVATIONS IN PRE - MARKETING CLINICAL STUDIES Sildenafil Phosphodiesterase 5 (PDE5) inhibitor Compound developed as an antihypertensive that produced improved erectile function in clinical trial patients Finasteride 5 alpha-reductase inhibitor repositioned from a benign prostatic hyperplasia treatment to male pattern baldness Dr. Prerana Manik Kadam 47
- 48. OBSERVATIONS IN POST - APPROVAL CLINICAL STUDIES Anticonvulsant Topiramate for bipolar disorder Anticancer Rituximab for rheumatoid arthiritis Aspirin for colorectal cancer Olanzapine in treatment of CINV Dr. Prerana Manik Kadam 48
- 49. EXPERIMENT VS IN SILICO APPROACH Dr. Prerana Manik Kadam 49
- 50. MIXED EXPERIMENTA L COMPUTATIONAL In silico Phenotype TYPES OF APPROACHES a) Profile b) Network c) Database Expression Chemical Clinical In vitro In vivo Clinical Dr. Prerana Manik Kadam 50
- 51. MIXED APPROACH • Both in-silico and experimental approaches • Result of computational methods is validated by pre- clinical biological experiments (in vitro and in vivo tests) and clinical studies. • Balanced - robust and logical approach ADVANTAGES - Greater success than discovery based on serendipity - Effective and rapid opportunities for repositioned drugs - Reliable Dr. Prerana Manik Kadam 51
- 52. SUCCESS STORIES Dr. Prerana Manik Kadam 52
- 53. FAILURE STORIES Dr. Prerana Manik Kadam 53
- 54. CHALLENGES Many false positives results from computational methods Data set integration difficult as data is • Complex • Unstructured New preclinical and/or clinical trials may be needed available data is unsatisfactory and does not comply with the requirements of regulatory agencies - IP protection is limited - This prevents some repositioned drugs from entering even into the market Dr. Prerana Manik Kadam 54
- 55. Using AI to integrate Drug repositioning Focus on Orphan diseases APPLICATIONS Development of Research sector of non industrial entities Diseases resistant to treatment Pandemic situations Precision medicine Dr. Prerana Manik Kadam 55
- 56. WHO Special Program for Research and Training in Tropical Diseases, Medicines for malaria venture, Drugs for Neglected Diseases Initiative Paromomycin and Miltefosine in Kala Azar Open Source Drug Discovery (OSDD) – Drugs for tropical infectious diseases 4000 genes for Mycobacterium tuberculosis and proteins for which they code Central Drug Research Institute- potent anti- HIV property of Thiazolidinones Previously Antibacterial INDIAN SCENARIO Dr. Prerana Manik Kadam 56
- 57. CONCLUSION Drug repurposing – advantageous. Therefore moving on from serendipitous/accidental discovery approach to a New ‘Balanced’ approach is necessary. Need for extensive research to create robust database with respect to drug and disease profiles Teach NEW tricks to an OLD drug Dr. Prerana Manik Kadam 57
- 58. THANK YOU Dr. Prerana Manik Kadam 58