From Combichem to Acceleration

of Medicinal Chemistry –

Making Way to Lead Optimization

Werngard Czechtizky

Sanofi-Aventis, Germany

November 25th, 2015

Combinatorial Chemistry – The 1990s

● Hypothesis: “Make More – Get More”

● Explore chemical space through vast compound collections through use of HTS

of drug targets derived from the new field of genomics

● But in spite of significant technological progress, …

● Synthetic output still much too small to cover chemical space

● Limited chemical diversity – insufficient for new lead discovery

● Slow elaboration of SAR within given compound class

● Ever more compounds of same type instead of true SAR

● Slow optimization progress in consecutive evolutionary cycles

● Ultimately, high costs per useful compound

Valuable Technology Developments – the 2000s

● Automated library synthesis & purification

● Adapted to medicinal chemistry and SAR generation

● Reducing burden of manual synthesis and purification of large numbers of

closely related products of ever-same reaction types

● Automatization of biological assays and HT biology

● HTS – uHTS, plate readers, Ionworks, etc

● Automated data processing and storage

● Large number of data for predictive modelling

● QSAR models etc

Further Progress for Medicinal Chemistry

● Advances in biology

● Rapid identification of many novel and exciting disease targets

● ´omics´ approaches

● Access to external opportunities – open innovation approaches

● Biophysical methods

● Advancements in computational chemistry and structural biology

● Increased ability to examine drug– protein interactions at molecular level

● Desktop protein and data visualization

● Improved computational efficiency

● Predictive modelling for in vitro physchem & ADME properties

• Improved Data Mining, Matched Pair analysis, QSAR

● Increase of expertise in multiple parameter optimization

● Expanded knowledge of desirable drug properties

● Support by CROs reduced costs and increased internal medicinal

chemists´ focus beyond synthetic challenges

M. Munson, Drug Discovery Today, 20 (8), 2015

C. Johnstone, Drug Discovery Today, 17(11/12), 2012

Current Challenge: Cycletime Reduction

● Traditional Medicinal Chemistry

● Often a sequential approach in which – because of cycletimes – the next

synthesis cycle is based on limited data

● Insufficient for true multiparameter optimization

● Quality improvement and cycletime reduction is a continuous topic

for pharmaceutical companies

● Optimized cycles of compound synthesis, QC, physchem, eADME and

biological profiling („design-make-analyze-test“)

● Optimized in vivo PK and safety profiling to reduce attrition in preclinical and

clinical phases

• Enhanced use of predictive models

● Major changes ● A new logistics process between chemistry, analytics and biology

● Integration of biological assays and new plating processes

● Parallel solubility, logD and chemical stability testing

● Parallel HT tests for Caco, microsomal lability and CYP inhibition

● Automatization of LC-MS validation

● Direct quality and concentration control of test samples

● Reduction of departmental interfaces

● Optimization of data flows between departments

● A change of mindset

● More data require better data management and focussed interpretation

Library

Synthesis Reg

More complex eADME Secondary assays etc

QC

prim Assay phys. Chem

eADME

IDG

3 4 5 7 9 12 Weeks Start of next round of synthesis

Optimization of „Design-make-analyze-test“ Cycles

Samples from Chemistry and Library Production

UV purity > XX True False

Split and reassemble plate

H-NMR LC-MS In vitro biology

Concentration of Stocks determined by NMR quantification

pKa optional

Thermodyn solubility

Chemical Stability

Log D optional

Microsomal lability

CACO

CYP inhibition

Biological Testing incl IC50

eADME profiling

physchem profiling

Modified Integrated Profiling Workflow

Optimization of Lead Compound X

● Compound X is a typical example of an early stage lead compound

in need of multidimensional compound optimization

● Decent biological potency and selectivity for a tool compound, but in vitro

and in vivo PK profile was clearly insufficient for further target validation

Outcome: Compound Y

● > 1000 novel (and diverse!) compounds synthesized and profiled by

new profiling workflow over 5 months

● No loss of potency for Compound Y

● Reduction of microsomal/hepatocyte clearance

● Overall cleaner profile on eADME parameters

● Cyp inhibition, induction, hERG, solubility

● In vitro and in vivo PK data sufficient for first pharmacological profiling

Attrition on the Way to Clinical Candidates

● Often due to re-ocurring problems with compound profiles

● Gaps in ADME, in vivo PK, safety profiles

• PK can´t be optimized for proper in vivo POP – no target validation

• Compounds don´t have high enough free fraction in target tissue

• Safety issues

• Often takes too long to understand background and how to optimize

• Lack of correlation between in vitro assays and in vivo tox data

• Serial in vivo tox profiling expensive and ethically questionable

● Consequences

● Leads insufficient for target validation

● Leads not optimizable to match candidate TPP

● Attrition needs to be tackled based on relevant in vitro and in vivo

data followed by their proper interpretation in PTs

● Optimization can be supported by historical data and predictive models

• Requires proper data access & data analysis

Step 1: Efficient In vivo PK/Safety Profiling

● ADME, PK & safety profiling integral part of drug discovery

● Exemplary workflow published by e.g. Novartis; analogous at Sanofi

* * Individual

additional in vitro

safety assays in

H2L and lead

optimization phase

not shown

Step 2: Optimization Using Predictive Modelling

● Desired: a global, living database and retrieval system of all available in vitro

and in vivo physchem, eADME, PK and safety data

● Useful for all scientists in a company to analyze various aspects of this data set

• Enabling optimization and better interpretation of in vivo PK and safety parameters

• Prerequisite for predictive modelling for PK and safety

Access to Living Extract DB

of all Sanofi compounds

● Chemical structure

● physchem data

● eADME data

● in vivo PK data

● Transporter data

● in vitro safety data

● in vivo Tox/TK data

● Predicted data

● Searchable and accessible for all Sanofi

scientists

● Data mining tools eg D360, Tox

Dashboard, eTox Sys

● Potential to be linked with external data

from collaborations/consortia

● Links to the different databases for

automatization

● Dynamic curation

Is Data Collection already it?

● Data aggregation is essential to avoid comparison of „apples & pears“

● Historically, many different data sources

• Individual test protocols in different companies – later merged

• Individual test protocols in different sites etc

● To be „hand“-curated for

• eADME: Caco2/PAMPA, microsomal lability, hepatic clearance, CYP inhibition

• physchem properties: aqueous solubility, logD

• In vitro safety assays (e.g. hERG)

• In-vivo PK and safety data

● Missing information needs to be added where feasible

● In vivo PK data historically often only partially available in electronic format

• Text mining

● Experimental gap filling may be needed for predictive modelling

● Lacking information on tissues, fraction unbound etc in electronic databases

● Usually even more pronounced for in vivo safety data

Extract DB & Data Retrieval

SAR Container /

ProjectCentricDataMart

Chemical Structures

Biological Data/in vitro safety data

Analytical/physicochemical Data

SARLab/ExtractDB Chemical Structures

Selected eADME Data

Selected PhysProp Data

In vitro safety data

In-vivo PK Data

In vivo tox Data

Current Retrieval

limited to SAR

Container

Pre-

Aggregated

Data

Data

● Data filtering & aggregation

● Focus on relevant assays with filtered and focussed

data relevant to analysis of PK and safety data

● Fast retrieval, reduced # of data points

Predictive Models

Data

Matched Molecular

Pair (MMP) Analysis

PTotal = 30 10^-7 cm/s

PTotal = 5 10^-7 cm/s

In vivo PK data

Transporter data In vitro safety &

In vivo tox data

Open to analysis for

all Sanofi scientists

How Data Will Support Medicinal Chemistry

● Predictive models ● Significant dataset easily accessible for data mining tools and early PKPD support

● Broad Data Access for Matched Pair Analysis

● Correlation of compound structures and properties with fraction

unbound, tissue distribution, PK/PD and toxicological profiles ● PK/ tissue distribution (unbound plasma and tissue concentration) to rationalize organ

exposure vs. pharmacological and tox effects

● Data to support PKPD and PBPK modelling

● Improved predictive safety models and guidelines for safety profiling ● ID of most effective safety panel, including validation of internal/external assays

● Enabling integration of e.g. external animal tox data to expand scope

● Expertise & data expansion in transporters ● Mapping of general tox effects and transporter data

● Supports medicinal chemists to understand PK and tox findings

● Equips PTs with guidelines when to test which transporters

Tissue Distribution – Where we stand

● Lipinski's Ro5 and structural

space beyond

● Recent years with development

of relatively broad knowledge of

transporters regulating uptake

and efflux of compounds

in/from various organs and

tissues

● Certain progress with • targeting of intestine, liver, kidney

and brain

• low absorbables and tumor

targeting – but mostly conjugates

● Still in development ● muscle and fat as target tissues

● avoidance of biliary excretion Eur. J. Pharm. Sc. 2006, 27, 425-46

Advancements in Brain penetration

● 3 barriers between blood and

brain

● BBB as largest surface area

for exchange

• Favoured route for global

delivery of drugs into brain

● Blood-CSF barrier

● Less important:

• Arachnoid barrier

● See e.g.

● Journal of Cerebral Blood Flow

& Metabolism 2012, 32, 1959–

1972

● NeuroRX 2005, 2(4), 541-553

● BMC Neurol. 2009; 9(1): 3ff Neurobiology of Disease 2010, 37,13–25

Addressing BBB by Compound Properties

● In vivo PK data have been

translated into compound

properties benefitial for brain

penetration ….

Intestinal Restriction – Low Absorbables

● Non-systemic drugs acting within the intestinal lumen without

reaching systemic circulation

● Additional potential to reduce toxic side effects

● First generation: polymeric resins that sequester phosphate ions,

potassium ions, or bile acids for the treatment of electrolyte

imbalances or hypercholesteremia

● Field has evolved towards non-absorbable small molecules or

peptides targeting luminal enzymes or transporters

● treatment of mineral metabolism disorders, diabetes,

gastrointestinal disorders, or enteric infections

● Non-systemic agents present new challenges, including

development of a better understanding and control of non-

transcellular leakage pathways into the systemic circulation

Curr Pharm Des. 2012,18(10): 1434–1445

Outlook

● Recent debate: „hot topics“ of Medicinal Chemistry

● Where will new validated targets come from?

• Can Chemistry contribute?

● New / revisited synthetic modalities

• Peptides, si/miRNA, conjugates (ADCs etc)

● Improved delivery, tissue targetting and early formulation

• e.g. oral bioavailability of peptides

• Understanding of the use of nanoparticles, polymers, transporters

● Stronger use of human dose prediction

● Contribution of Chemistry to chemical probes and stem cell

research

● Enhanced collaboration with External Sourcing/Opportunities

● Early safety profiling of SMs

the challenge remains…

Acknowledgements

LG-CR FF Chemistry Martin Will Peter Below Maik Fredersdorf Michael Lottra Thomas Maier Peter Monecke Günter Schneider Peter Hamley Laurent Bialy Erich von Roedern

LG-CR FF Lead Identification Technologies Norbert Krass Thomas Licher Hartmut Mors Everard Pap Heinrich Rojak DSAR FF Andreas Czich Friedemann Schmidt Alexander Amberg Markus Kohlmann Niels Griesang Ulrich Kürzel Alexander Marker Katharina Mertsch Jakob Olsen

FF SDI Team Dan Crowther Gerhard Hessler Hans Matter Joachim Meyer Dan Dragos Stefanescu

FF IS Team Fritz Roll Ludwig Franzisket