session 1-1 william potter.pdf

8/10/2019 Session 1-1 William Potter.pdf

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Project Phase

ProjectSanction

ProgramSanction

Program Phase

ProductDecision

Product Phase

Overall Drug Discovery and

Development Process

Target

Identification

and

Validation

Assay

Development

Lead

Generation

QSV

Goals

Hypothesis

GenerationCandidate Development Commercialization

Phase

III

Submit Global

LaunchGlobal

Optimization

LeadOptimization

FirstHuman

Dose

PhaseIA

PhaseIB/II


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Areas of Collaboration In Drug Development

Pre-ClinicalDevelopment

Phase 1 Phase 2 Phase 3

FHD

FEDLaunch

Compound

Screening

Lead

Optimization

Biology of Disease- Target ID

- MOA - Validation

Clinical Studies

- Consistent phenotyping

- Large-scale prospective trials

Biomarkers - Synthesis - Application

Phase 4

Target ID

Target Validation

Biobanks - Collection - Storage


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NIH Clinical Center,

CTSAsNIH Molecular Libraries

Initiative

TRND

FDA

Re-

view

Ph. IIIPh. IIPh. IDiseaseTarget

ID

Assay

Dev.HTS

Probe

to

Lead

Pre-

Clinical

FDA

IND

NIH Supported

Basic Research

CER

New NIH-FDA Partnerships

RAID

HMORN

PCORI

Where Does this Fit into The NIH NCATs Effort?

Target Validation Space


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Hits provide a suggestive relationship of molecular structure and biological

activity to the targetConfirmed intrinsic activity (DR, IC50, EC50, etc) and characterization against secondary assays.

Confirmed structural identity and purity.

Meets pre-defined criteria for further assessment. Usually of limited potency/selectivity.

Provides starting point for SAR/structural hypotheses to improve biological potency/selectivity and drug-properties.

Rarely suitable for in vivostudy or advanced mechanistic studies

Target

Active

Hit

Lead

Key Definitions in Lead Generation

Active defines a "substance" that meets a threshold level of activity in a

primary screen

Associated with a HTS

Activity level defined by precedent and screen performance

Structure and purity of screening substance not confirmed

A Lead represents a compound series derived from a Hit that demonstrates a

relationship between chemical structure and target-based activity in biochemical

and cell-based models. Compounds within the series have physicochemical

properties, potency and selectivity deemed appropriate for in vivoevaluation.

Common Characteristics

nM primary activity

Sub uM activity in cell based assays

10x selectivity for related targets

Enhanced solubility, chemical and metabolic stability, evidence of in vivoexposure


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High quality Leads are derived from high quality hits.

The key to finding high quality hits depends on a successful match of lead

generation strategies and technologies to a target. Broad screening of random

compounds is often not the most productive approach.

Optimizing quality hits into quality leads requires a relevant and validated

biological testing paradigm.

The quality of a testing paradigm in optimizing hits to leads is ultimately

limited by the quality of iterative ligand hypotheses (via SAR) tested against it.

Viable leads very rarely fall out of a single screening eventwith a fixed library.

Cross-functional discipline integration and team drug discovery experience

play a large role in successful lead discovery. Duplicating this outside of some

sort of portfolio optimization organization such as academia is likely to require

substantially longer timelines with lower success rates.

Lead GenerationKey Observations


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A col laborat ive enterpr is e among Discovery Scient ists for:

the selection of valid targetsfor drug discovery,

the design and implementation of relevant screens andtestingparadigmsthat will enable compound discovery and

for industry, the identification and optimization of proprietarymoleculesthat demonstrate potential to be refined into drugcandidates.

Hypothesis to Lead Generation


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2000-2001

Probabilistic Knowledge Based

Lead Discovery Science

1996-1999

Dominant I nf luences

HTS Driven Hit Discovery

Combi-Chem

Maximum Diversity

Screen for Drug Promise

Target Validation Science

Bioinformatics

Target Platforms

Target Drugability

Quantitative Biology

Assay QualityADMET Profiling

Structural Sciences

IP Strategies

Biological Flowschemes

2004-2013

Learning

Technology

Diversified Lead Generation

Strategies

Computational Science

Library Sciences

Compound Quality

Targeted DiversityChemoinformatics

Receptor Panel Profiling

Chemogenomics

Systems Biology

Dominant I nf luences

Factored into Contemporary Lead Generation


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Knowledge BasedProbabilistic

Lead Generation Strategy

Target and

Gene Family Precedent

Little or No Discovery

Precedent

High Throughput Screening with

Maximal Diversity

Alternative Diversity (Natural Products)

Triage of Targets

Gene Target Platforms

I n SilicoHypothesis Generation

Computationally driven diversity

Targeted Libraries

Rational Design considering structural

biology and/or known ligands

Paradigm for Selection of Lead Generation Strategy

Poor historical performance

Library and assay quality are key variables

Follow-up paradigm can be complex depending

on quality of diversity and target biology

Higher p(TS) of Hit identification

Not all targets benefit equally from this approach


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Using integrative approaches and data to identify targets and biomarkers

from publicly available GWAS data

Published SNP

strongly associatedwith disease. Causal

gene not evident

Candidate causal genes and

pathways of disease with human

evidence

DRUG TARGET OR BIOMARKER

Causal Networks

OtherInformative

data

eSNPs

Raw public SNP data with weaker

associations

Pathway information

Genetics: mouse & human

RNA: multiple tissueseQTLs (SNP choice)

cQTLs

RNA-RNA correlations

Causality

Networks


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What Level of Characterization and

Optimization defines a Viable Lead?

Primary Assays

Binding/ Functional Assays

Biochemical/Cellular

Selectivity/Cytotoxicity

In silicoFilters

Secondary AssaysSelectivity:subtype/target family

Complex Cellular Assays

ADMET Surrogates

Tox Surrogates

BioPharm Surrogates

Tertiary AssaysADME

Animal Models

Surrogate Activity

Efficacy Model

Toxicology

BioPharm

ADME

I n Vivo Pharmacology

Toxicology

I n Vitro

Advanced I n Vitro

Attribute Profiling

Active

Lead

Components of a Biological Flowscheme

Hit

SAR

Clinical

Candidate


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Lead Optimization

Lead (molecules)

candidates are

identified through invitro and in vivo testing

Potency and selectivity

are enhanced

LeadOptimization

Optimize


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Phase IA (Single dose safety studies)

Single dose studies

escalating/increasing

if well tolerated

Often can establish

MTD (maximally

tolerated dose)

Primary concern is to

establish safety

PhaseIA

PhaseIB/II

PhaseIB


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Phase IB (Multidose Safety Studies)

Drug is administered in multipleescalating doses to a relativelysmall number of healthyvolunteers and/or patients todetermine safety, steady-state

PK and establish MTD forsubsequent studies

Increasing use of phase IB toprovide evidence of efficacyand/or hitting biochemical

target (use of biomarkers andsurrogate endpoints).Sometimes extends to POC(proof of concept) small clinicaltrials (also called IIa)

PhaseIB


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Proof of Concept (POC)

Definition:

The Development Plan that defines the minimum number of experiments/studies that

provides the critical data that are required to fundamentally change (increase or

decrease) the p(TS) sufficiently to drive a Go/No Go Decision

Key parameters:

Clinical efficacy, receptor occupancy, safety and tolerability, drugability

Typically includes:

Small controlled study,


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4/11/2013 15

The Leading Edge: FAST Contracts

FAST-AS: Autism spectrum FAST-PS: Psychotic disorders spectrum

FAST-MAS: Mood-Anxiety spectrum

Similar language for all three solicitations


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4/11/2013 16

FAST Contract (Under Final Review)

Goals: Enhanced understanding of underlyingmechanisms and development of innovative treatments

Early phase clinical trials (FIH, POCM)

New compounds and repurposed drugs

Experimental medicine paradigm of Fast-fail trials

Aligned with RDoC: focus on fundamentalmechanisms that cut across traditional diagnosticcategories (e.g., working memory)


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4/11/2013 17

Weighing priorities for targets & compounds

Robust pre-clinical data

Identifying top molecular targets

Clinical targets: e.g., RDoC domains/constructs

Receptor occupancy: PET ligand availability

Functional measures: e.g., EEG/ERPs, PPI

IND

Extant clinical dataPhase I or beyond


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Phase III (Registration Quality Trials)

Pivotal Efficacy: At least two positive (usually

requires several to achieve)

Double blind safety & efficacydata

Broader Safety Data (>1500pts):Can be open to address:

Age & Gender

Duration (1 yr or more)

Special populations (especially ifPK issues)

Characterizing Response Patients most likely to respond

Optimal dosing schedule

Switching treatments, etc.

PhaseIII


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Program TeamLeader

MarketResearch,

MarketPlanning

Discovery Regulatory

ClinicalPharmacology

Therapeutic Area

Medical MD/ClinicalResearch Administrator

(CRA)

Toxicology

Absorption,Distribution,Metabolism,

Excretion (ADME)

ToxicologyProjectLeader

DevelopmentProject

Management

Clinical ProjectManagement

QualityAssurance

CM&CRegulatory

Clinical TrialMaterial

ProductDevelop-

mentProcessDevelop-

ment AnalyticalMedicalSystems

Quality

AffiliateMedical

DataManagement

Typical Program Team

session 1-1 william potter.pdf

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