医薬品の臨床試験予測：フェーズIII結果予測のための革新的ベイズ法ツール

In recent years, there has been an explosion in predictive technologies to help researchers select only the most promising candidates for clinical development. The need for such tools is driven by the disastrous economic consequences of late-stage failures, which account for over 60% of all drug terminations. This report describes a powerful and novel predictive tool called Bayesian network modeling and demonstrates its application in clinical forecasting.

• Reduce drug development costs
• Increase median cumulative 7-year revenue per Phase III trial
• Redirect capital and human resources to development programs with the greatest likelihood of success
• Expose clinical trial subjects to fewer unsafe or ineffective drugs
• Improve the accuracy and decision-making utility of market forecasts (which currently assume that all drugs in the projection period will achieve NDA approval)
• Increase industry' s and society' s confidence in including pediatric subjects in clinical trials

Moreover, unlike existing predictive technologies such as microdosing, toxicogenomics, or ultra high-throughput screening (HTS), all of which entail significant costs in capital equipment, training, and ongoing maintenance, clinical forecasting based on Bayesian statistics is comparatively inexpensive.

Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes begins by summarizing existing predictive technologies with particular reference to their limitations. Gene expression arrays, while providing useful prognostic information, are limited by the lability of mRNA and inconsistencies across microarray platforms. Microdosing is disadvantaged by limited databases required for the studies, unclear regulatory guidelines, and, in the case of PET studies, short trace half-lives and limited ability to distinguish between the compound and its metabolites.

With complete transparency as to data sources and assumptions, the authors show how the Bayesian network model predicted outcomes (NDA approval or failure) based on an independent dataset of 503 new chemical entities (NCEs) with an optimal accuracy of 78%. The author emphasizes that, with more complete and historical datasets of in vivo and in vitro compound data including therapeutic index ranges, the model' s performance can be even further improved.

Table of Contents

Existing Predictive Tools for Pharmaceutical Forecasting

• Biological Tools
  • Biomarker and Target Discovery via High-Throughput Genomics and Proteomics
  • Bioinformatics: High-Throughput Biomarker and Target Discovery
  • In Silico Drug Discovery with the Connectivity Map
  • Pharmacogenetics and Pharmacogenomics
  • High-Throughput Screens and Animal Models
• Clinical Tools
  • Therapeutic Index
  • Pharmacokinetics
  • Population Pharmacokinetics
  • Pharmacokinetic Models
  • Microdosing
    • Sidebar: Phase IV Postmarketing Surveillance
• Bayesian Market Forecasting and Modeling of Cost-Effectiveness in Drug Development

Description of a Bayesian Clinical Forecasting Model

• Application of a Bayesian Network to Clinical Forecasting in Drug Development
  • Prior Probability of NCE Success and Failure
  • Conditional Probability Tables
  • Training Dataset from Tufts CSDD Sources
  • Independent Dataset Construction
  • Model Evaluation Shows 78% Accurate Prediction of NCE Success on
  • Independent Dataset
• Existing Predictive Tools Empower Bayesian Clinical Forecasting
  • Well-Designed Clinical Forecasting Models Can Boost Accuracy of Market
  • Forecasts
  • Biomarkers and Clinical Predictors Empower Bayesian Forecasting Tools

Case Study: Recombinant Human Activated Protein C, Eli Lilly' s Xigris

• Data Used For Forecast
• Model Predicts Xigris Has Low Probabilities of Clinical Success, Safety and Efficacy

Economic Impact of Bayesian Clinical Forecasting

• Pharmacoeconomic Evaluation
  • Monte Carlo Simulation to Determine Expenditures and Revenues for BN Model and for Pharmaceutical Industry
  • Model Reduced Median Expenditures, Increased Median Cumulative 7-Year Revenues
  • Harnessing the Power of Late-Stage Failure Data and of Industrywide Data Sharing
  • Data Storage Issues: Paper vs. Digital

Societal Impact of Bayesian Clinical Forecasting

• Impact on Children
• Impact on the Elderly

Appendix A

• Brief Overview of Bayesian Networks

Appendix B

Glossary

• Table 1.1. Advantages of Zebrafish in Drug Development
• Table 4.1. Impact of 78% Accurate Clinical Forecasting on Public Companies
• Table 1A. Preference Table for Value Node in Figure 1A

• Figure 1.1. Example of a Pharmacokinetic Profile
• Figure 1.2. Role of Bayesian Networks in Phase IV
• Figure 2.1. Clinical Variables Believed Most Crucial to NCE Clinical Success
• Figure 2.2. Overview of Algorithm for Constructing Leaf Node CPTs
• Figure 2.3. Clinical Forecasting Models Empower Market Forecasts
• Figure 3.1. Prior and Posterior Probability Distributions: Clinical Success for rhAPC
• Figure 3.2. Prior and Posterior Probability Distributions: Safety and Efficacy for rhAPC
• Figure 3.3. Effect of Setting Prior Bias to "Optimistic" on Prior and Posterior Probability Distributions: Clinical Success for rhAPC
• Figure 5.1. Societal Impact of Widespread Adoption of Accurate Clinical Forecasting Methods
• Figure 5.2. Time Lag from Initial NDA Approval to Pediatric sNDA Submission
• Figure 1A. A Simple Influence Diagram (ID)
• Figure 2A. A 3-Layer Bayesian Network