Two recent scientific developments, one in biostatistics and one in pharmacogenomics are likely to have a major impact on the design and monitoring of phase III and seamless phase II/III oncology trials, greatly improving their chances of success. Advances in human genomic studies have shown that many common mutations have prognostic and predictive value for identifying patients who are likely to benefit from a molecularly targeted agent. At the same time there has been a surge of interest within the biostatistics research community in the design of adaptive clinical trials. An adaptive trial is one in which early data obtained from the trial itself can be used to modify the future course of the trial, without undermining its integrity or statistical validity (Gallo et. al., 2006a, 2006b). Adaptive designs play a role in both early and late stages of clinical drug development. Our interest, however, is in late stage confirmatory trials (late phase II and phase III), where the goal is to improve the chances for regulatory approval of a new medical compound. The overall failure rate of compounds even at this late stage is 45\%, and for oncology trials the failure rate is almost 60\% (Kola and Landis, 2004). It is worth noting that by this time significant proportions of the costs of discovering and developing a drug have been incurred. Among the many causes for this attrition, a major one is choosing the wrong population for the test drug. It is becoming increasingly apparent that treatment effects can differ greatly between different genomic patient subsets. We wish to promote a new type of design for confirmatory trials in oncology.in which we use the fact that predictive markers can identify patients who are sensitive to distinct therapeutic agents, such that patients with a positive marker might benefit differentially from the targeted therapy compared to patients with a negative marker. Predictive markers provide the opportunity to conduct so called population enrichment designs (Temple, 2005). Genomic technologies such as microarrays and single nucleotide polymorphism genotyping may be used to identify the marker status of patients during the screening phase of a trial. If a marker is considered predictive for the test drug one could in principle restrict enrollement to the subset of patients carrying the favorable genotype, thereby enriching the study population and increasing the chance of a successful trial. Our goal is to develop statistical software that will support these types of designs. The software will utilize the concept of two-stage adaptive designs in which the results at the first stage may be used to enrich the population at the second stage if the biomarker is predictive.
Public Health Relevance: This project will support the development of new research methods and software for oncology trials in which predictive biomarkers may be used to enrich the population as the second stage of the design based on results observed at the first stage.
Thesaurus Terms: Active Follow-Up; Arm; Biometrics; Biometry; Biometry And Biostatistics; Biostatistics; Calendar; Cancer, Oncology; Clinical Drug Development; Clinical Trials; Clinical Trials, Unspecified; Communities; Computer Programs; Computer Software; Data; Designing Computer Software; Development; Drug Development, Clinical; Drug Testing/Development, Clinical; Drugs; Enrollment; Event; Flr; Failure (Biologic Function); Futility; Future; Genetic; Genetic Alteration; Genetic Change; Genetic Defect; Genomics; Genotype; Goals; Human; Human, General; Investigation; Libraries; Link; Logic; Man (Taxonomy); Man, Modern; Medical; Medication; Method Loinc Axis 6; Methodology; Methodology, Research; Monitor; Mutation; Patients; Pharmaceutic Preparations; Pharmaceutical Preparations; Pharmacogenomics; Phase; Play; Polymorphism, Single Base; Population; Population Study; Predictive Value; Probability; Programs (Pt); Programs [publication Type]; Randomized; Research; Research Methodology; Research Methods; Role; Sbir; Sbirs (R43/44); Snp; Snps; Subgp; Sample Size; Screening Procedure; Simulate; Single Nucleotide Polymorphism; Small Business Innovation Research; Small Business Innovation Research Grant; Software; Software Design; Solutions; Specific Qualifier Value; Specified; Staging; Statistical Methods; Subgroup; System; System, Loinc Axis 4; Technology; Testing; Therapeutic Agents; Time; Upper Arm; Work; Writing; Base; Biomarker; Clinical Investigation; Computer Program/Software; Cost; Design; Designing; Develop Software; Developing Computer Software; Drug Detection; Drug Testing; Drug/Agent; Enroll; Failure; Flexibility; Follow-Up; Genome Mutation; Hazard; Improved; Interest; Oncology; Prognostic; Programs; Prototype; Public Health Relevance; Randomisation; Randomization; Randomly Assigned; Screening; Screenings; Social Role; Software Development; Statistics/Biometry; Success; Treatment Effect; User-Friendly
