Access to Research Date 11-08-2018 Venue Conference HAll NIAS IISc campus Conference and workshops for clinical practitioners to introduce them to modern tools and an alternative approach to modern scientific research. Purpose 1. Build a network of physicians across the country 2 Train physicians to analyse clinical data and restructure it to make it compatible with research standards 3. Introduce modern tools to understand the mechanism of actions of medicine 4. Introduce artificial intelligence and machine learning to clinical practitioners to support decision-making processes Access to Science Clinical experience and traditional knowledge are important sources of data that affect decision making processes in modern healthcare systems. This data should be made accessible for scientific evaluation and validation to improve healthcare worldwide. The Open Source Pharma Foundation believes that clinical practitioners from various disciplines should have the right to access research so that they can help identify problems, contribute their scientific knowledge, and support the discovery ecosystem. Background The majority of medical practitioners working on the ground level with patients do not take part in open clinical research worldwide. However, the data collected and owned by them plays an important role in establishing better discovery pathways. Through this workshop, we seek to open opportunities to enhance health care systems around the world and to overcome the following challenges faced by medical practitioners. 1. Regulatory limitations 2. Academic limitations 3. Time constraints 4. Lack of access to modern tools

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 < 0.5M 0.2 0.5
Selectivity over kinase 2 > 50 100 30
Inhibition of phospho-Kinase 1 in ABC cells IC50 < 5M 2 5
No cytotoxicty in ABC cells IC50 > 50M >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