Biomaterial Database

A Bayesian adaptive design for estimating the maximum tolerated dose curve using drug combinations in cancer phase I clinical trials. 2017

Mourad Tighiouart, and Quanlin Li, and André Rogatko

Samuel Oschin Comprehensive Cancer Institute, 8700 Beverly Blvd., Los Angeles, CA, 90048, U.S.A.

We present a cancer phase I clinical trial design of a combination of two drugs with the goal of estimating the maximum tolerated dose curve in the two-dimensional Cartesian plane. A parametric model is used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity. The model is re-parameterized in terms of the probabilities of toxicities at dose combinations corresponding to the minimum and maximum doses available in the trial and the interaction parameter. Trial design proceeds using cohorts of two patients receiving doses according to univariate escalation with overdose control (EWOC), where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent. The maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. Performance of the trial is studied by evaluating its design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD curve and under model misspecifications for the true dose-toxicity relationship. The method is further extended to accommodate discrete dose combinations and compared with previous approaches under several scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

UI	MeSH Term	Description	Entries
D009369	Neoplasms	New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.	Benign Neoplasm,Cancer,Malignant Neoplasm,Tumor,Tumors,Benign Neoplasms,Malignancy,Malignant Neoplasms,Neoplasia,Neoplasm,Neoplasms, Benign,Cancers,Malignancies,Neoplasias,Neoplasm, Benign,Neoplasm, Malignant,Neoplasms, Malignant
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D004305	Dose-Response Relationship, Drug	The relationship between the dose of an administered drug and the response of the organism to the drug.	Dose Response Relationship, Drug,Dose-Response Relationships, Drug,Drug Dose-Response Relationship,Drug Dose-Response Relationships,Relationship, Drug Dose-Response,Relationships, Drug Dose-Response
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D000970	Antineoplastic Agents	Substances that inhibit or prevent the proliferation of NEOPLASMS.	Anticancer Agent,Antineoplastic,Antineoplastic Agent,Antineoplastic Drug,Antitumor Agent,Antitumor Drug,Cancer Chemotherapy Agent,Cancer Chemotherapy Drug,Anticancer Agents,Antineoplastic Drugs,Antineoplastics,Antitumor Agents,Antitumor Drugs,Cancer Chemotherapy Agents,Cancer Chemotherapy Drugs,Chemotherapeutic Anticancer Agents,Chemotherapeutic Anticancer Drug,Agent, Anticancer,Agent, Antineoplastic,Agent, Antitumor,Agent, Cancer Chemotherapy,Agents, Anticancer,Agents, Antineoplastic,Agents, Antitumor,Agents, Cancer Chemotherapy,Agents, Chemotherapeutic Anticancer,Chemotherapy Agent, Cancer,Chemotherapy Agents, Cancer,Chemotherapy Drug, Cancer,Chemotherapy Drugs, Cancer,Drug, Antineoplastic,Drug, Antitumor,Drug, Cancer Chemotherapy,Drug, Chemotherapeutic Anticancer,Drugs, Antineoplastic,Drugs, Antitumor,Drugs, Cancer Chemotherapy
D000971	Antineoplastic Combined Chemotherapy Protocols	The use of two or more chemicals simultaneously or sequentially in the drug therapy of neoplasms. The drugs need not be in the same dosage form.	Anticancer Drug Combinations,Antineoplastic Agents, Combined,Antineoplastic Chemotherapy Protocols,Antineoplastic Drug Combinations,Cancer Chemotherapy Protocols,Chemotherapy Protocols, Antineoplastic,Drug Combinations, Antineoplastic,Antineoplastic Combined Chemotherapy Regimens,Combined Antineoplastic Agents,Agent, Combined Antineoplastic,Agents, Combined Antineoplastic,Anticancer Drug Combination,Antineoplastic Agent, Combined,Antineoplastic Chemotherapy Protocol,Antineoplastic Drug Combination,Cancer Chemotherapy Protocol,Chemotherapy Protocol, Antineoplastic,Chemotherapy Protocol, Cancer,Chemotherapy Protocols, Cancer,Combinations, Antineoplastic Drug,Combined Antineoplastic Agent,Drug Combination, Anticancer,Drug Combination, Antineoplastic,Drug Combinations, Anticancer,Protocol, Antineoplastic Chemotherapy,Protocol, Cancer Chemotherapy,Protocols, Antineoplastic Chemotherapy,Protocols, Cancer Chemotherapy
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D017321	Clinical Trials, Phase I as Topic	Works about studies performed to evaluate the safety of diagnostic, therapeutic, or prophylactic drugs, devices, or techniques in healthy subjects and to determine the safe dosage range (if appropriate). These tests also are used to determine pharmacologic and pharmacokinetic properties (toxicity, metabolism, absorption, elimination, and preferred route of administration). They involve a small number of persons and usually last about 1 year. This concept includes phase I studies conducted both in the U.S. and in other countries.	Clinical Trials, Phase I,Drug Evaluation, FDA Phase I,Evaluation Studies, FDA Phase I,Human Microdosing Trial,Phase 1 Clinical Trial,Phase I Clinical Trial,Phase I Clinical Trials,Clinical Trials, Phase 1,Drug Evaluation, FDA Phase 1,Drug Evaluation, FDA Phase I as Topic,Evaluation Studies, FDA Phase 1,Human Microdosing Trials,Microdosing Trials, Human,Phase 1 Clinical Trials,Microdosing Trial, Human,Trial, Human Microdosing,Trials, Human Microdosing