Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes : Market Research Report

Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes

Published by: CHI Insight Pharma Reports

Published: Jul. 1, 2007 - 74 Pages

Section 1

Biological Tools

Existing Predictive Tools for Pharmaceutical Forecasting

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

Section 2

Application of a Bayesian Network to Clinical Forecasting in Drug Development

Description of a Bayesian Clinical Forecasting Model

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

Section 3

Model Predicts Xigris Has Low Probabilities of Clinical Success, Safety and Efficacy

Section 4

Pharmacoeconomic Evaluation

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

Data Used For Forecast

Economic Impact of Bayesian Clinical Forecasting

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

Section 5

Impact on the Elderly

Appendix A

Glossary

Tables

Societal Impact of Bayesian Clinical Forecasting

Impact on Children

Brief Overview of Bayesian Networks

Appendix B

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

Figures

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

Abstract

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. Among its many potential benefits, clinical forecasting can:

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.

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Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes

Table of Contents

Abstract