Welcome and Opening Remarks

1
Welcome and Opening Remarks
AAPS Workshop Entitled Optimization of
Drug-Like Properties During Lead Optimization.
Ronald T. Borchardt Department of
Pharmaceutical Chemistry The University
of Kansas
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Drug Discovery Paradigm of the 1990s
Hit to Lead
Lead Optimization

• New Chemistry
• Combinatorial Chemistry
• Informatics Driven Chemistry

High Affinity Ligands OR Drug Candidates
?
Structural Hit
Structural Lead

• New Biology
• Genomics
• Proteomics

High Throughput Biological Screens and
Bioinformatics
Optimization of the biological activity through
Combinatorial Chemistry , Parallel Synthesis and
Structure-Based Drug Design (Molecular Modeling
and Crystallography)
3
Definitions
High Affinity Ligand A molecule that has high
binding affinity and specificity for a
macromolecule (protein, DNA, RNA) but does not
have drug-like properties. Drug Candidate A
molecule that has high binding affinity and
specificity for a macromolecule (protein, DNA,
RNA) but also has drug-like properties(i.e.,
solubility, permeability, chemical/enzymatic
stability etc).
4
Drug Discovery/Development Paradigm of the 1990s
Big Pharma
Drug Candidate
Drug Candidate
X
X
Input
Input
Recipe for Failure!!!!!
5
Drug Discovery/Development Paradigm of the 1990s
Biotechnology Companies
Drug Candidate
Drug Candidate
X
X
X
X
Input
Input
Recipe for Failure!!!!!
6
Because of a Lack of Input from Development
Scientists, Discovery Scientists Tended to Fall
into The High Affinity Trap
HTS Hit, Ki10 µM
Optimization Using only receptor- based and
cell-based assays
NO !
High Affinity Ligand, Ki1 nM
Can drug-like characteristics be built back
into a high affinity ligand?
What is the probability of success of a high
affinity ligand in preclinical and clinical
development?
VERY LOW !
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Why Do Compounds Fail in Preclinical/Clinical
Development?
Study1

• Lack of Efficacy - 30 (46)
• Animal Toxicity - 11 (17)
• Adverse Effects in Man - 10 (16)
• Commercial Reasons - 5 (7)
• Miscellaneous- 5 (5)
• Pharmacokinetic- 39 (7)

is based on N 198 NCEs () is based on N
122 NCEs (excludes anti-infectives)
T. Kennedy, Drug Discovery Today, 2, 436-444
(1997).
Note drug-like properties highlighted in red.
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Why Do Compounds Fail in Preclinical/Clinical
Development?
Study2

• Lack of Efficacy - 28
• Preclinical Tox./Clinical Safety - 44
• Commercial Reasons - 19
• ADME- 9

Scott Biller (Novartis), personal communication.
Note drug-like properties highlighted in red.
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Drug- Like Properties

• Physicochemical
• Solubility
• Chemical stability
• Hydrophobicity/hydrogen bonding potential
• Charge
• Size
• Salt form
• Polymorphism

• Biological
• Intestinal mucosal cell permeation
• Liver and kidney clearance
• Metabolism
• Transporters
• Protein binding
• Blood-brain barrier permeation
• Target cell permeation
• QT interval prolongation (e.g. HERG)
• Toxicity

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Drug Discovery Paradigm of the Future
Lead Optimization
Hit to Lead

• New Chemistry
• Combinatorial Chemistry
• Informatics Driven Chemistry

High Affinity Ligands that are Drug Candidates
Structural Hit
Structural Lead

• New Biology
• Genomics
• Proteomics

High Throughput Biological Screens and
Bioinformatics
Optimization of the biological activity and the
drug-like properties through Combinatorial
Chemistry , Parallel Synthesis and
Structure-Based Drug Design (Molecular Modeling
and Crystallography)
11
The Drug Discovery/Development Paradigm of the
Future will be Highly Integrated!
Drug Candidate
Preclinical Development
Clinical Development
Discovery Research
Input
Recipe for Success!!!!!
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AAPS Profiling Workshop May, 2003
Quantitative
Semi-Quantitative
Time/Effort
Qualitative
Information about Drug-Like Properties

Accurate Data
No Data
Candidate Selection
Lead Optimization
Hit to Lead
Guidance to Discovery Scientists
Guidance to Development Scientists
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Pharmaceutical Profiling in Drug Discovery for
Lead Selection

• Volume 1 of the Biotechnology Pharmaceutical
  Aspects series
• Edited by Ronald T. Borchardt (University of
  Kansas), Edward H. Kerns (Wyeth Research),
  Christopher A. Lipinski (Pfizer Global RD
  retired), Dhiren R. Thakker (University of North
  Carolina), and Binghe Wang (Georgia State
  University)
• Based on a 2003 AAPS workshop of the same name
• Contributors include highly experienced leaders
  from both industry and academia, with
  specialization in advanced methods for in silico,
  physiochemical, permeability, in vitro, and ADME,
  as well as in medicinal chemistry applications on
  pharmaceutical property information

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Semi-Quantitative
AAPS Optimization Workshop September,
2004
Quantitative
Time/Effort
Qualitative
Information about Drug-Like Properties

Accurate Data
No Data
Candidate Selection
Lead Optimization
Hit to Lead
Guidance to Discovery Scientists
Guidance to Development Scientists
15

What drug-like property assays should be
included in the test funnel during lead
optimization and how should the resulting data be
used in drug design?
Test Funnel
Drug-Like Property Assays
A B C D E F G
Solubility Assay
Receptor Binding Assay
Protein Binding Assay
Cell Based Assay
Pgp Assay
?
Tox. Assays
Proof of Concept in Animals
Metabolic Stability Assay
Chemical Stability Assay
Cell Permeability Assay
CYP450 Inhibition Assay
Drug Candidate
16

What is the appropriate balance between
pharmacological and drug-like properties?
Pharmacological Properties
Drug-Like Properties
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Optimization of Drug-Like Properties During Lead
Optimization
AAPS WORKSHOP
September 19-22, 2004 l Hilton Parsippany l
Parsippany, NJ

• 
  Workshop Goals and Objectives
• Provide a program of quality podium presentations
  from leaders in the field, poster presentations
  from the audience and vendor exhibits.
• Promote discussion among a sophisticated,
  multidisciplinary, and participatory audience.
• Promote the exchange of information and the
  discussion of views between pharmaceutical
  development and drug discovery scientists.
• Through this discussion, contribute to the
  development of tools for the prediction and
  measurement of drug-like properties and
  application of this information to the
  optimization of drug discovery leads.
• Ultimately, to contribute to the improvement of
  the properties of drug candidates entering
  development.

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Optimization of Drug-Like Properties During Lead
Optimization
AAPS WORKSHOP
September 19-22, 2004 l Hilton Parsippany l
Parsippany, NJ

• 
  

• Organizing Committee
• Ronald T. Borchardt, University of Knasas
• Edward H. Kerns, Wyeth Research
• Michael J. Hageman, Pfizer, Inc
• James L. Stevens, Eli Lilly and Company
• Dhiren R. Thakker, University of North Carolina
• Co-Sponsoring Organizations
• ACS-Division of Medicinal Chemistry
• ACS-New Jersey Section
• American Society of Clinical Pharmacology and
  Therapeutics
• European Federation for Pharmaceutical Sciences
• International Society for the Study of
  Xenobiotics
• Society of Toxicology