Therapeutic Bench to Beside:
Art & Science Of Drug Discovery and Development
(and everyone’s role in it)
Theodore F. Reiss, M.D.
1
Nancy Whorf
2
Message/Objectives
• Therapeutic innovation : impactful, practical,
real world application
– Integrated, complex system, non-linear thinking,
iterative, learning, collaborative, team centered.
• An overview approach
– Model
– Some specifics
– Challenges and opportunities for the future
3
The 3T’s
Dougherty, D. et al. JAMA 2008;299:2319-2321
The Innovation Interface
• Basic/clinical science
• Technology focus
• Public health
• Policy/ Culture
Copyright restrictions may apply.
“Language” of Biomedical Innovation
•Certainty of Benefit/Risk --vs•Partially justified new knowledge
“Bench To Bedside” : Interdependence
• Centered on collaborative, synergistic scientific efforts
• Resource intensive, necessitating efficiency
Innovation or Stagnation: FDA March 2004
[----------------------”Bench to Bedside”------------------]
5
Traditional Concept: Novel Mechanism
Discovery
Pre-clinical Toxicology
• In vitro
• Understand pathobiology
• Target ID/validation
• In vivo
• Molecule ID
• Initial regulatory interactions
• Start formulation development
Phase II
• POC
• Dose selection
• Safety in patients
• Initial benefit/risk profile
• Formulation completed
• Finalize alignment with
regulatory agencies
Phase III
Phase I
• SD/MD safety
• PK/PD characterization
• Target engaged
• Biologic effects
Phase IV/V
• Post marketing requirements
• Efficacy confirmation
• Post marketing safety
• Outcomes
surveillance
• Safety confirmation
• Detailed benefit/risk profile • New indications
• Health economic data for
reimbursement authorization
6
Strategic Development
Metaphor: Financial Planning
• Define the goal
• Identify the component parts
• Develop a plan working backwards from the goal
• Plan depends on many factors:
– Define level of benefit/risk
– Determine interim steps that have to be achieved
– Flexibility to adjust to environmental changes
7
“Bench to Bedside”: Principles
• Neither simple nor linear
• Begin with the goal: Unmet medical needs-public health value
– Data: Clinical, regulatory, and health economic
• Demonstrate clear, population specific, benefit / risk
– Efficient and timely as possible
• Dynamic, responding to new knowledge
8
Principles: Optimize Potential for Success
• Disease area focus
– Multiple targets and/or molecules within a target
• Strategic scientific development plan
– Begin with goal and design backwards
• Failure the norm
– Go/no go criteria to exit early if risk/benefit unacceptable
– Kill early
• Critical importance of predictive safety & efficacy biomarkers
– Patient identification
– Response prediction
• Apply learning iteratively
9
Drug Development Paradigm (Better!)
Discovery
•
Toxicology
Phase I
Phase II
Phase III
Goal
“System” Approach:
–
–
–
–
–
Neither simple, nor linear
Each component is part of a “whole” strategy
“Goal” driving earlier development steps: Iterative
• Address unmet medical need
• Demonstrate clear, population specific benefit/risk
• Efficient and timely as possible
Dynamic, responding to new knowledge
Collaborative : Integrated Project Team - Many Experts “Thinking as One”
10
C
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“Begin With The End (Goal) In Mind”
First Principle – Clinical Vision
•
•
•
•
•
•
•
Patient population (or sub population)
Efficacy “threshold”
Tolerability profile
Method of delivery
Potency
Dosing interval
Drug Interaction
Create public health value with optimum benefit /risk in a defined patient population
Could refocus goal further during Phase I-III
For example: optimal responder population identified
11
Efficiency Gain From A Thoughtful Scientific/Regulatory
Strategy
Unoptimized Strategy
Discovery
Preclinical
Preclinical
Toxicology
Toxicology
Phase
Phase
I I
Phase II Phase II
Phase III
Phase III
Time (Years)
Regulatory Filing
Patent life
12
Efficiency Gain From A Thoughtful End-to-End Strategy:
Kill Early
Discovery
Preclinical
Toxicology
Phase I
Phase II
Phase III
Time (Years)
If don’t meet POC
or ePOC Kill
Regulatory Filing
Patent life
13
Less Optimal Approaches
• Therapeutic goal not clear
• No planning: Stumbling forward one experiment to the next
• Single molecule focused only
Why?
• Time/resource limitations
• Design to goal
– Solving unmet need with acceptable benefit/risk
– Molecules are means to ends
14
Advanced Concepts
• Development strategy optimizes for:
– Fewest resources
– Least time
– Most information (scientific, clinical, regulatory, health economic)
Milestones
• Go/no go
– Scientific probability of achieving “goal”
– Investment decision points
• Key regulatory agency interactions
– Strategy aligned with regulatory agency perspective
15
Conceptual Model
Linking Drug Development with Value
PUBLIC HEALTH
Unmet Medical Needs
Cost vs. Effectiveness (or Utilities)
EXTERNAL
BIOLOGY
DISCOVERY
PH I
PRECLINICAL
TOXICOLOGY
PH III
GOAL
TPP
VALUE
PH II
INTERNAL
NO GO
NO GO
NPV
16
Advanced Concepts: Target Discovery
• What makes development programs efficient?
• Why are some disease areas easer than others for drug
discovery and development?
• What are optimal characteristics of a “molecular target”
for a therapy?
17
Pathobiology Of Disease: Clear Understanding
Critical To Optimize Development
• Foundation for basic-preclinical-clinical consistency
• Facilitate safety and efficacy prediction
– Provides potential for clearly defining “subpopulations”
• If clear:
– Target rich (osteoporosis)
• If speculative:
– Targets are high risk
– Predicting efficacy and safety and defining “subpopulations” much more challenging
18
Osteoporosis: Available Knowledge Allows
for “Efficient” Development
•
•
•
•
Pathobiology of disease reasonably well understood
Animal models available
Animal models predictive of human disease
Natural history of disease known
– Large cohort studies
– Clinically relevant endpoint defined and outcomes known
– Establish and improving clinical disease biomarkers
• Investigational therapies affect biology of disease
“Efficient” system to develop therapies, investigate new tools
to predict response, and identify sub-populations
19
Respiratory Development Is Challenging
• Pathobiology is less well understood (relative to
osteoporosis)
• Disease definition is imprecise
• Animal models are imprecisely predictive
• Natural history, outcomes less are defined
• Criteria defining clinical response difficult to determine
20
Example: Failure of Prediction
DP1 Antagonist in Asthma/Allergic Rhinitis
• Receptor for PGD2
• PGD2 released from mast cells with histamine/CysLT’s
– Pharmacologic activity in airways of asthmatics
• DP1 knock-out – blocks response in mouse OVA model
• DP1 blockade inhibits inflammation in guinea pigs
• Polymorphism in DP1 receptor associated with asthma
• However:
– No effect of receptor antagonist in asthma/allergic
rhinitis
21
Target Identification and Validation
• Address unmet medical needs?
• Understand pathobiology/pathway regulation
– Pathology, genetic pathways linked
– Pharmacologic studies in disease models
• Knockouts, transgenic, pathway interruption (siRNA, antibodies, small molecules)
– Predict benefit/risk
• Link to man for prediction (altered expression of target or biomarker)
• Probability of activity different from the target –Safety
– Biomarker development
• Determine molecular approach and delivery optimization
– Clone target /tractable
• Design molecules
– Iterative process
22
Pre-Clinical Biology
Target
Pre-clinical
Discovery
Toxicology
• Generate and select optimal compound
– Potency
– PK in animals
– “Probe” safety study
– Determine if formulation possible
• Unique challenges by type and route
• Difficulty under appreciated
• Alternate strategy: unoptimized molecule to POC in man ASAP
– Other strategies
23
Pre-clinical Toxicology
General Concepts
• In vitro and in vivo studies to predict tolerability in man
• Examples: In vitro toxicology
– Carcinogenic potential / metabolic profile/ P450 studies
• Examples: In vivo (at least two species)
– Single dose, multiple dose: 1 week – 1 year, carcinogenicity studies
– Dose or time related toxicities
• Component of strategic development plan
– Facilitate strategy: Phase I-III trials (length, sequence)
– Must provide adequate dose exposure margin
24
Phase I – IIa: Critical Bridge - Iterative Optimization
• Demonstrate:
– PK, target engagement, biological activity, initial clinical benefit /risk
– Go/no go
• “Learning phase”
– Clinical experimental models to optimize target molecules
– “Hypothesis” generating trial(s) to:
• Optimize subsequent clinical experiments
• Identify and validate predictive markers or sub-populations
• Time intensive, speed not primary concern
– Biomarker(s) to optimize dose selection/prediction of benefit or risk
• Target engagement (example: receptor occupancy: NK1)
• Target engagement and biologic effect (example: urinary LTE4: 5LO)
• Target engagement and biologic effect and clinical surrogate endpoint
(example: reticulocytes - EPO)
25
Imaging as a Biomarker
Target Engagement and Dose NK1 Antagonist
Binding of PET
tracer to NK1
receptors
Blockade of
NK1 receptors
after aprepitant
dosing
Brain NK1 Receptor Occupancy (%)
Mean (± SE) Plasma Trough Concentrations
40/25
125/80 375/125
100
90
80
70
60
50
40
30
20
10
0
0
1
10
100
1000 10000
Aprepitant Plasma Trough Concentration (ng/mL)
Tracer Binding
Low
Hargreaves J Clin Psych 63: (suppl 11): 18-24, 2003
High
26
LTE4 % Predose (Mean ± SEM)
Example: Biomarker 5-LO FLAP Inhibition
Urinary LTE4
140
120
Placebo
25 mg
50 mg
125 mg
250 mg
500 mg
100
80
60
40
20
0
0-3
3-6
6-9
9-12
24
Hours Post dose
36
48
72
27
Dose Selection: The Phase II Activity
• Identify dose-response relationship
• Benefit/risk: must be determined in a defined population,
through an adequate dose range
- Must demonstrate minimal or no effect
• Goals: Identify minimal dose achieving maximal response
without evidence of dose limiting toxicity
Response
100
Efficacy
Safety
50
0
Dose
28
Example: Biomarker Leading to Dose Ranging
CysLT1 Antagonist
Clinical Pharmacology & Therapeutics 1997; 61:(1) 83-92
29
Phase III: Characteristics
• Program design considerations
– Sufficient to address clinical questions in targeted population
(use in clinical practice)
• Multiple or few trials? If worldwide, special considerations
• One dose optimal
– All measurements must have been previously “validated” and
“qualified” according to stringent standards
– Placebo vs. comparative designs
– Tolerability: Pooling data – continued “signal” detection
– Endpoint: outcome or surrogate? (CASS Study example)
• Health economic data: public health / reimbursement issues
• Cost - Effectiveness/ Cost - Utility
30
Regulatory Considerations
• Consultations
– “Buy-in” to Phase III plans before starting
• Endpoints/validation/statistical plan
• Data submissions
– Worldwide submissions to regulatory agencies
• Content: scientific/clinical rationale, individual trial data, “integrated
summaries” (safety and efficacy)
– Biggest grant proposal you ever submitted! (5-15 trials [4,000-20,000 patients])
– Draft label included
– Recommendation for patient information/post marketing surveillance
• Labeling
– Scientific and risk/benefit data summarized
– Negotiated separately with agencies worldwide
– Draft label early in development: target product profile/promotion
31
Post Marketing Safety Surveillance
• Major emphasis for the future:
– Example: “Sentinel” initiative
• Spontaneous reports
– Different rigor among countries
– Claims databases
– Data very difficult to interpret
• Causal relationships difficult to determine
• New methods of “signal detection”
• Pooled clinical trial database (including Phase IV)
32
Efficient, Timely Execution Of Development
Programs: Project Team
• Forms early: target discovery
– Describe goal, develop plan, iteratively manage plan
– Efficient, fast, scientifically excellent
– Effectiveness: integration of scientific/regulatory information in a
hypothesis driven, sequenced plan (No go decision points)
• High performance team (synergism)
• Individually experts in separate disciplines
– Broad scientific/regulatory knowledge
•
•
•
•
Members know all roles and responsibilities
Anticipate others’ needs/thoughts
Always think two steps ahead for self and other team members
Leadership
33
Research & Discovery Process
It takes ~10-15 years and $802 million to develop one new
medicine1
PostMarketing
FDA
Review
1
2
5 Compounds
6
250 Compounds
1.5
Phase III
n=1000-5000
Clinical
Trials
Phase II
n=100-500
Phase I
n=20-100
Preclinical
Drug
Discovery
5,000 – 10,000 Compounds
5
Years
1DiMasi
JA, Hansen RW, Grabowski HG Journal of Health Economics2003, 22, 151-185
34
19
8
19 4
8
19 5
8
19 6
8
19 7
8
19 8
8
19 9
9
19 0
9
19 1
9
19 2
9
19 3
9
19 4
9
19 5
9
19 6
9
19 7
9
19 8
9
20 9
0
20 0
0
20 1
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
20 0
09 8
(E
)
50
#'s NCE's Approved
40
40
30
30
20
20
10
10
0
Source: PhRMA, FDA
No. NCE's approved
60
No. NCE s Approved
’
R & D Spend- PhRMA Members (in $B)
R&D Expenditure PhRMA Members (in $B)
Despite Substantial Investment,
New Products Infrequent
NCE's approved and R&D Costs since 1984
R&D Spend (in $B)
60
50
0
35
What are Today’s Challenges?
• Greater, efficient output
– Increasingly complex system/environment
• No consensus on efficient process improvement
• Poor predictive ability to identify targets with adequate
benefit/risk
• Development knowledge/world view is siloed
• Art of development, regulatory, translational science not broadly
known
• Collaborative efforts stifled
• Investment capital disappearing
Some Causes For Fewer New Therapies
• Simple, “low hanging ” targets have been developed
– Biology is complex
– Multiple, difficult to dissect pathways
• Accurate prediction of safety and efficacy based on the molecular
target has proven elusive
• Better benefit/risk required, difficult to predict
– Unmet medical needs more narrowly defined - phenotypes
– To demonstrate efficacy, “Clinical Outcome” frequently required
• Rather than surrogate endpoint
• Greater need for validation
– Less tolerance of side effects
• Common and infrequent
• Need to demonstrate value to multiple stakeholders (payers)
37
Challenges In Discovery & Development
• Greater efficacy and better safety necessitate:
– Pathobiology of disease must be accurately
understood (pick the right targets)
– New tools for better safety and efficacy prediction
• Must increase probability of success at each step
– Larger trials required
• Costs increasing rapidly, resources limited
• Demonstrate cost/effectiveness benefit
– Specific “sub-populations” to enhance benefit risk
• Population identification
• Response prediction
38
How Should “Bench to Bedside” Evolve And
Who Will Lead The Way?
• Bayh-Dole Act
– Allowed University to patent research discoveries
– Potential to create “wealth” for the universities
(institutes)
– Have had a effect on the conceptual models of the
future of translational science
39
Path Forward: Improving “Bench to Bedside”
– Position Statements
• National Institute of Medicine
– Clinical Research Round Table
• NIH
– “Road map for medical research”
• National Academy of Sciences
– “Exploring strategies for future research”
• FDA
– Critical Path Initiative
40
Key Elements of Position Statements
• Collaboration among Pharma, academic, government
– Share knowledge (FDA, NIH & Pharma) to develop new
science through consortiums
– Extensive and complementary databases
– Similar initiatives overseas
• “Bench to Bedside” is complex, true progress will require
integrated solutions
• Need to teach team collaboration science
41
Conflict of Interest (COI)
• COI issues are real
– Focus on financial only
– Tone: non – collaborative
– Unintended consequences – effecting collaborative efforts
• Debate needs broader view: goal - improve public health
• Alternative position
–
–
–
–
Include all types of conflicts
Improve public health is a primary interest
Checks and balances in place
Trust can’t be developed unless balance achieved
• Collaboration/ cooperation/ learning /teaching
• Examples in the public good: Bell Labs / IMI
42
Where are We Going?
• Vision?
• Scientific “paradigm shift”?
• Increasing realization that institutions can’t go it
alone
• Collaborative efforts in complex systems
• Translational development science
• Focus on unmet medical need/public health value
• Sharing the rewards
– Bayh/Dole
• New commercial models
Future Vision:
Integration/Collaboration
•
Sci Transl Med 7 April 2010: Vol. 2, Issue 26, p. 26cm12
Models and Policy Choices
•
•
•
•
•
•
•
•
Shared science
Cross-institution development & business models
Shared continuous improvement
Broad as well as deep scientific knowledge
More transparency
More phenotype specific therapies
Cost /effectiveness
Broader, balanced ethics discussions
Discovery & Development
•
Complex system, goal oriented, integrated
– Optimally, create public health value & maximize benefit/ risk
– Non-linear
– Iterative/responsive to new information
• Many moving parts: constant problem-solving/management
• Optimally, development phases predicatively linked
• Resource intensive: optimally efficient, disciplined; predictable biomarkers
& surrogate outpatients to determine POS and Go/No Rules
• Collaborative, experienced project team
• Progress will require cross-institutional collaboration
46
Nancy Whorf
47
Concepts: Reviewed
• Neither simple nor linear
• Goal: Unmet medical needs – public health value
– Data: Clinical, regulatory and health economic
– Demonstrate clear, population specific benefit / risk
– Efficient and timely as possible
• Dynamic, responding to new knowledge
• Disease area focus
– Multiple targets and/or molecules within a target
• Strategic scientific development plan
– Begin with goal and design backwards
• Failure the norm
– Go/no go criteria to exit early if risk/benefit unacceptable
– Kill early
• Critical importance of predictive safety & efficacy biomarkers
– Patient identification
– Response prediction
• Apply learning iteratively
48
Formulation Research and Development
• Is it feasible to synthesize the compound and
develop a formulation?
• Difficulty under appreciated
• Unique challenges:
By type: “small molecule”, vaccine, protein
antibody, siRNA, gene
By route: Oral, inhaled, IV, topical, etc.
49
Phase I-II: General Concepts
• Phase I:
– Tolerability/pharmacokinetics/efficacy
• Tolerability issues affect benefit /risk or limit dose determination
- Possible No Go
– Biomarker(s) to optimize dose selection/prediction of benefit or risk
• Target engagement (example: receptor occupancy: NK1)
• Target engagement and biologic effect (example: urinary LTE4: 5LO)
• Target engagement and biologic effect and clinical surrogate endpoint
(example: reticulocytes - EPO)
• Phase II
– Significant strategy choices: selection of surrogate biomarkers and
endpoints
– Help in bracketing doses for final clinical dose ranging study
– Predictive of ultimate clinical endpoint (outcome)
– Help to identify responder populations and biomarkers
– Experimental models in humans
50
Project Teams: Additional Principles
• High performance team
– In order to facilitate collaboration/synergism
• Members know all roles and responsibilities
• Anticipate others’ needs/thoughts
• Always think two steps ahead for self and other
team members
– Listen to others’ points of view
– Debate and dialogue respected and valued
51
Future Approaches: Systems Biology
Nature Genetics 2005: 37; 710-17
52
Dose Ranging: M3 Antagonist Example
European Respiratory Journal 2006; 28: p772-780
53
Phase I – IIa: Critical Bridge
Pharmacodynamics/Proof Of Concept
•
Demonstrate:
–
•
Identify benefit/risk
–
•
Go/no go
Understanding safety profile important
–
•
PK, target engagement, biological activity, initial clinical benefit
Strategy about commitment illness, medicines
Significant strategy choices: selection of surrogate
biomarkers and endpoints
–
–
–
–
Help in bracketing doses for final clinical dose ranging study
Predictive of ultimate clinical endpoint (outcome)
Help to identify responder populations and biomarkers
Experimental models in humans
54
Major Goals: Phase III and Beyond
• In population(s) of interest
– Document tolerability in more patients for longer time
– Confirm benefit/risk
– Potentially outcome / health economic data
• Plan for post approval safety monitoring
• Plan for and execute new indication(s)
55
Dose Selection: Phase II Activity
•
Necessary inputs to formal dose ranging study(ies)
– Tolerability response curve or known limitations (Phase I/IIa)
– Pharmacokinetics, biomarker of target engagement or activity
– Dose spacing
•
Strategic options to obtain data
– Standard dose ranging study/dose adaptive design/modeling
•
Other strategic options
– Combine dose ranging and POC
– Dose ranging on surrogate endpoint or outcome
56
Other Phase III Considerations
• High costs
– Example: 15K patient outcome study: $400M over 4 years
– Resources becoming limited
• Recruitment of patients globally
– Different approaches to medicine
• What is the basis for a Phase III Program “go”
– Strategic options
• Importance of achieving profile
• Partnering
• Funding
57
Example: Serendipity
Anti-Leukotriene (CysLT1) Program
• History: Nobel prize: classical physiology, pharmacology
• Many attempts before final candidate “evolved”
– Receptor Antagonists
• 100 Compounds
• 10 Animal Toxicology
• 5 Compounds in Man
• 5-LO Program (Both Direct and Indirect Inhibitors)
– 50 Compounds
– 5 Animal Toxicology
– 2 Compounds in Man
• Took too long/too many iterations
• Efficient development program
58
Example: Dose Ranging - CysLT1 Antagonist
European Respiratory Journal 1998 11(6) 1232-1239
59
Phase III: Characteristics (con’t)
• Trial design consideration
– Usually single dose from Phase IIb
– Statistically rigorous: clear hypothesis, pre-established data
analysis plan, no post hoc data dredging
– “Clinically important” treatment effect targeted
– Safety monitoring boards becoming standard
– Non-inferiority designs
– Real world designs/health economic data
– Inclusion criteria (generalizability)
– Exposure to as many patients and for as long as possible
60