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Opensourcepharma Dr Nibedita rath
- 1. My Perspectives of Medicinal Chemistry and a few case studies Nibedita Rath, PhD Scientific Director Open Source Pharma Foundation Bangalore
- 2. Acknowledgements Open Source Pharma Foundation
- 3. Keywords Active / Hit / Lead Lead Generation Lead optimization Lipinski’s rule Lipophilicity, logP/logD Molecular matched pair High throughput screening ADME properties Ligand Efficiency Lipophilic ligand efficiency Structure activity relationship Fragment based lead generation Mutagenecity Bioisostere Drug repurposing Connectivity Map
- 4. Drug Discovery and Development Profile Find Hits Profile Optimize Early Clinical Nomination (ECN) / Candidate Drug (CD) Lead Generation Lead Optimization ECN Preclinical Development Market Phase I Phase III Phase II Identify Target • Average Time to bring a compound to market: 10-12 Years • Average cost: $ 2.5 billion
- 5. Target Identification What disease are we trying to treat? Can an understanding of the disease show us how to interfere with the disease process? Genome Exploratory biology Literature Known drugs Is there precedent for small molecule inhibitors – or would an antibody work?
- 6. A Drug… •Meets (unmet) medical need •Not adversely toxic for the disease it is treating Potent in vivo, correct duration: low dose Selective for the target •Can be manufactured •Patentable A Lead High affinity for the target in vitro Depends on optimal molecular properties (shape, electronics) for interaction with active site – determined by structure-activity relationships (SAR) What makes a Drug All depend on the properties = structure of the compounds
- 7. Drug-like and Lead-like Drugs Lipinski “Rule of 5” Absorption-permeation MWt 500 ClogP 5 H bond donors 5 H bond acceptors 10 Leads • Reduced molecular complexity • MWt 350 • ClogP 3 • H bond donors 5 • H bond acceptors 8 Adv Drug Deliver Res, 1997, 23, 3 J Chem Info Comput Sci, 2001, 41, 1308 J Chem Info Comput Sci, 2001, 41, 856 J Med Chem, 2002, 45, 2615 Nature Rev Drug Discovery, 2004, 3, 660 We know what we need - how do we find it?
- 8. Lead to Candidate Drug Small leads will need to be “grown” to add potency, Large leads are much rarer and will have to be very potent or need “trimming” to make it to drug candidates. -300 -200 -100 0 100 200 300 400 0 100 200 300 400 500 600 Change in Mol Wt in going from Lead to Drug Mol Wt of Lead 480 ‘Lead-Drug Pairs’ (from W. Sneader, Drug Prototypes & Their Exploitation, Wiley, 1995)
- 9. What else varies with logP? logP Binding to enzyme / receptor Aqueous solubility Binding to P450 metabolising enzymes Absorption through membrane Binding to blood / tissue proteins – less drug free to act Binding to hErg heart ion channel - cardiotoxicity risk Log P needs to be optimised
- 10. * Drug-like properties: guiding principles for design-or chemical prejudice? Leeson, P.D.; Davis, A. M.; Steele, J. Drug Discovery Today: Technologies, 2004, 1(3), 189-195. High Throughput Screen (HTS) Full company collection Natural products Or Sub-set e.g lead-like physical properties* Radom Screening Rational Design Pharmacophore / Structural Focus search on a directed set of compounds defined using structural knowledge of target protein and/or known ligands Fast Follower Known drug / Competitor compound Analyse competitors chemical series: Is there any weakness? Is there scope for novel IP? Lead Generation Approach chosen will depend on the target information available
- 11. criteria Series 1 Series 2 BIOSCIENCE Potency against kinase 1 IC50 < 0.5M 0.2 0.5 Selectivity over kinase 2 > 50 100 30 Inhibition of phospho-Kinase 1 in ABC cells IC50 < 5M 2 5 No cytotoxicty in ABC cells IC50 > 50M >50 >50 CHEMISTRY Acceptable LogD 0 - 3.5 3.4 2.1 Acceptable Molecular weight < 450 455 321 Appropriate free drug fraction PPB < 98 % 96 99.1 Thermodynamic solubility >50uM (crystalline) 0.5 35 Intellectual Property Patents filed Patents filed Patents filed Adequate chemical synthesis Good routes available No issues No issues Which compound is the ‘best lead?’ Lead Generation - what’s the best series?
- 12. Lead Optimisation How do we convert the initial lead into a potential drug? Improve binding to the target Physical properties eg solubility Synthesis Optimise pharmacokinetics how much gets in after usually an oral dose Models of the disease Patents is it in our own, someone else’s or is there the potential for new IP? Safety – is it toxic in models of toxicity? Manufacture
- 13. 13 Lead Optimization: Medicinal Chemistry Practice Hypothesis Design Make Biological testing Analyses Make Test Cycle • Hit compounds from screening • Build hypothesis based on available knowledge. • Design compounds to improve potency and/or other properties. • Synthesize the compounds • Test the biological activity. • Analyse the results and build new hypothesis. • An itierative cycle continues till the team solves all the issues related to potency, safety and pharmacokinetics. • A typical lead optimization program required 20-50 iterative make-test cycles to reach a clinical candidate.
- 14. A few examples of Safety/Toxicity issues during lead optimization hERG liability Off-target activity Kinase selectivity CYP inhibition Genotoxicity / mutagenecity Reactive metabolites
- 15. Examples from Classical Drug Discovery
- 16. Proton Pump Inhibitor: The story of Nexium • Omeprazole/Losec: Used for treatment of gastric acid disorder – heartburn, peptic ulcer • Discovered by Astra: 1989 – one of the best selling drug in late 90s. • Omeprazole: A proton pump inhibitor, is a pro-drug. •Omeprazole showed inter-individual variability. Some patients needed higher or multiple doses to achieve the relief and healing. Olbe et al. Nature Reviews Drug Discovery, 2003, 2, 132. Active form of Omeprazole
- 17. Proton Pump Inhibitor: The story of Nexium• New discovery programs to improve the efficacy of omeprazole and increase oral bioavailability. • Discovered that the S-enantiomer of omeprazole is more efficacious. Later this became one of the most selling drugs of AstraZeneca, Nexium. Olbe et al. Nature Reviews Drug Discovery, 2003, 2, 132.
- 18. Example of Structure based Design: Gleevec • Gleevec/Imatinib: Used for the treatment of multiple cancers, mostly chronic myleloid leukomia (CML). • Marketed by Novartis (first approved in 2002): makes > $5 billion per year. • Identified BCR-ABL gene as the target found only in leukaemic cells. • Medicinal chemistry aided by structure based design played a vital role in achieving potency and selectivity. Starting point Potency and selectivity Pharmacokinetic properties Capdeville et al. Nature Reviews Drug Discovery, 2002, 1, 493.
- 19. Example of Structure based Design: Gleevec Crystal Structure of BCR-Abl – Protein tyrosine Kinase bound to Gleevec Presumably the first drug discovered from rational drug design http://www.rcsb.org/pdb/home/home.do Pdb code: 3GVU
- 20. Case Study : Mitigating mutagenicity
- 21. Aromatic Nitro compounds and its mutagenic liability • Nitro group is less preferred in medicinal chemistry because of its mutagenic liability. • Several nitroarenes are shown to be non-mutagenic and are in clinic. • Pretomanid (PA-824) and Delamanid (OPC-67683) have shown excellent results in clinic as anti-TB drugs. Panda et al. ChemMedChem 2016, 11, 331.
- 22. Literature database search showed two interesting matched pairs • Modulation of stereoelectronic properties of nitro group Proposed Derivatives ChemMedChem 2016, 11, 331.
- 23. Drug Repurposing
- 24. Drug Repurposing – A promise of rapid Clinical impact at a lower cost Attractive and Pragmatic Large number of potential drugs never reach clinical testing Approved or failed drugs with established safety profile, finding new indications can be rapidly bring benefits to patients Successful drug repurposing Cyclooxygenease inhibitor Aspirin for coronary-artery disease Antiemetic thalidomide to treat multiple myeloma Successes thus far have been mostly serendipitous Current academic and industrial efforts Broad institute, Boston, USA – gene expression profiling Cure Within Reach, Chicago, USA Exscientia, Dundee, UK – AI: polypharmacology and phenotypic screening NovaLead, Pune, INDIA: repurposing generic drugs
- 25. A drug repurposing Hub: http://www.broadinstitute.org/repurposing Nat. Med. 2017, 23, 405. • Gene expression profiling has enabled recent repurposing discoveries. • Sirolimus for leukemia • Topiramate for inflammatory bowel disease (IBD) • Imipramine for small-cell lung cancer • Rapamycin, immunosuppressant, used to prevent organ transplant rejection. Anti-epilepsy drug Anti-depressant
- 26. Connectivity Map Gene-expression profiles derived from the treatment of cucltured human cells with large number of perturbabens. Lamb et al. Science 2006, 313, 1929.
- 27. Attrition in Drug Discovery and way forward
- 28. Attrition of Drug Candidates Waring et al. Nat. Rev. Drug. Discov. 2015, 14, 475. AstraZeneca; GlaxoSmithKline; Pfizer; Eli Lilly * 812 oral development compounds
- 29. Summary What Makes a Drug? Target Identification Lead Generation Lead Optimisation Oral dosing of drugs Lipophilicity – A Key Drug Property Drug-like and Lead-like • Lead Generation Approaches – High Throughput screening – Rational Design – Lead Generation Libraries • Ligand Efficiency • Lead Optimization • Case Studies • Molecular matched pairs • Fragment based lead generation • Bioisostere replacement • Mutagenecity • Drug Repurposing • Attrition in clinical candidates • Link to physicochemical properties