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Objective: To evaluate the economic impact of a Bayesian network model designed to predict clinical success of a new chemical entity (NCE) based on pre-phase III data., Methods: We trained our Bayesian network model on publicly accessible data on 503 NCEs, stratified by therapeutic class. We evaluated the sensitivity, specificity and accuracy of our model on an independent data set of 18 NCE-indication pairs, using prior probability data for the antineoplastic NCEs within the training set. We performed Monte Carlo simulations to evaluate the economic performance of our model relative to reported pharmaceutical industry performance, taking into account reported capitalized phase costs, cumulative revenues for a postapproval period of 7 years, and the range of possible false negative and true negative rates for terminated NCEs within the pharmaceutical industry., Results: Our model predicted outcomes on the independent validation set of oncology agents with 78% accuracy (80%sensitivity and 76% specificity). In comparison with the pharmaceutical industry's reported success rates, on average our model significantly reduced capitalized expenditures from $727 million/successful NCE to $444 million/successful NCE (P < 0.001), and significantly improved revenues from $347 million/phase III trial to $507 million/phase III trial (P < 0.001) during the first 7 years post launch. These results indicate that our model identified successful NCEs more efficiently than currently reported pharmaceutical industry performances., Conclusions: Accurate prediction of NCE outcomes is computationally feasible, significantly increasing the proportion of successful NCEs, and likely eliminating ineffective and unsafe NCEs.

Context: In the past, cancer patients entering phase 1 studies confronted the prospects of high risk and unlikely benefit. Over the last decade, cancer drugs under development have become more targeted, and the clinical research environment has become more scrutinized. The impact of these changes on the risks and benefits to patients who participate in phase 1 cancer trials is unknown., Objective: To determine trends in the rates of treatment-related (toxic) death, objective response, and serious toxicity and to identify factors associated with these outcomes., Data Sources: We searched abstracts and journal articles reporting the results of phase 1 cancer treatment trials originally submitted to annual meetings of the American Society of Clinical Oncology (ASCO) from 1991 through 2002., Study Selection: We focused on published single-agent trials that enrolled patients with advanced solid tumors and excluded studies testing agents already approved by the US Food and Drug Administration at the time of the ASCO presentation., Data Extraction: Multiple observers independently extracted information on trial design, location, sponsorship, types of tumors treated, drug class, route of administration, and clinical outcomes., Data Synthesis: The overall toxic death rate for 213 studies (involving 6474 cancer patients) published in peer-reviewed journals was 0.54%, while the overall objective response rate was 3.8%. Toxic death rates decreased over the study period, from 1.1% over the first 4 years of the study (1991-1994) to 0.06% over the most recent 4-year period (1999-2002) (P<.01). Response rates also decreased but by proportionally much less. After adjusting for characteristics of the experimental trials and the investigational agents, the odds of a patient dying from an experimental treatment while participating in a trial submitted during the most recent 4-year period were less than one tenth those of a patient participating in a trial submitted during the first 4-year period (odds ratio, 0.09; 95% confidence interval, 0.01-0.67; P = .009). In comparison, the adjusted odds of a patient experiencing an objective response over the same time periods decreased by approximately half (odds ratio, 0.46; 95% confidence interval, 0.32-0.66; P<.001)., Conclusions: The level of risk experienced by cancer patients who participate in phase 1 treatment trials appears to have improved over the 12-year period from 1991 through 2002. Because toxic death rates have decreased more quickly than have objective response rates, the ratio of risk to benefit may have also improved. These changes relate in part to the targeted and less-toxic nature of newer cancer drugs and are coincident with the increased attention that has been paid to the safety of clinical research over the time period we analyzed.

Purpose: To review characteristics of contemporary phase III oncology trials and create an explicit framework to help clinical researchers prioritize novel therapies for phase III testing., Methods: We searched the MEDLINE and EMBASE databases for all reviews of phase III trials; cataloged all phase III trials in two national clinical trial databases; and reviewed approval criteria of recently approved oncology drugs from public data provided by the US Food and Drug Administration. Industry data not available elsewhere in the medical literature were obtained from a sourcebook published by a large contract research organization., Results: Phase III oncology trials are the most expensive and time-consuming aspect of the drug development process. The results of these trials continue to exert the greatest influence on the treatment decision of oncologists and remain pivotal to the granting of drug approval. Making optimal decisions about which agents to advance to phase III testing may decrease the overall cost of cancer drug development and limit the number of patients exposed to ineffective drugs. A conceptual decision model for prioritizing novel therapies for phase III testing is presented., Conclusion: Cancer drug development has become more complex and expensive, whereas overall clinical progress remains slow. The transition from phase II to phase III requires a strategic decision that is based on new considerations. A greater investment in phase I and II drug trials may be required to provide the information necessary for phase III planning.