An in silico approach helped to identify the best experimental design, population, and outcome for future randomized clinical trials.

OBJECTIVES: The main objective of our work was to compare different randomized clinical trial (RCT) experimental designs in terms of power, accuracy of the estimation of treatment effect, and number of patients receiving active treatment using in silico simulations. STUDY DESIGN AND SETTING: A virtual population of patients was simulated and randomized in potential clinical trials. Treatment effect was modeled using a dose-effect relation for quantitative or qualitative outcomes. Different experimental designs were considered, and performances between designs were compared. One thousand clinical trials were simulated for each design based on an example of modeled disease. RESULTS: According to simulation results, the number of patients needed to reach 80% power was 50 for crossover, 60 for parallel or randomized withdrawal, 65 for drop the loser (DL), and 70 for early escape or play the winner (PW). For a given sample size, each design had its own advantage: low duration (parallel, early escape), high statistical power and precision (crossover), and higher number of patients receiving the active treatment (PW and DL). CONCLUSION: Our approach can help to identify the best experimental design, population, and outcome for future RCTs. This may be particularly useful for drug development in rare diseases, theragnostic approaches, or personalized medicine.

Quantitative prediction of the impact of drug interactions and genetic polymorphisms on cytochrome P450 2C9 substrate exposure.

BACKGROUND AND OBJECTIVE: Cytochrome P450 (CYP) 2C9 is the most common CYP2C enzyme and makes up approximately onethird of total CYP protein content in the liver. It metabolises more than 100 drugs. The exposure of drugs mainly eliminated by CYP2C9 may be dramatically modified by drug-drug interactions (DDIs) and genetic variations. The objective of this study was to develop a modelling approach to predict the impact of genetic polymorphisms and DDIs on drug exposure in drugs metabolised by CYP2C9. We then developed dosing recommendations based on genotypes and compared them to current Epar/Vidal dosing guidelines. METHODS: We created two models. The genetic model was designed to predict the impact of CYP2C9 polymorphisms on drug exposure. It links the area under the concentration-time curve (AUC) ratio (mutant to wild-type patients) to two parameters: the fractional contribution of CYP2C9 to oral clearance in vivo (i.e. CR or contribution ratio), and the fractional activity of the allele combination with respect to the homozygous wild type (i.e. FA or fraction of activity). Data were available for 77 couples (substrate, genotype). We used a three-step approach: (1) initial estimates of CRs and FAs were calculated using a first bibliographic dataset; (2) external validation of these estimates was then performed through the comparison between the AUC ratios predicted by the model and the observed values, using a second published dataset; and (3) refined estimates of CRs and FAs were obtained using Bayesian orthogonal regression involving the whole dataset and initial estimates of CRs and FAs. Posterior distributions of AUC ratios, CRs and FAs were estimated using Monte-Carlo Markov chain simulation. The drug interaction model was designed to predict the impact of DDIs on drug exposure. It links the AUC ratio (ratio of drug given in combination to drug given alone) to several parameters: the CR, the inhibition ratio (IR) of an inhibitor, and the increase in clearance (IC) due to an inducer. Data were available for 80 DDIs. IRs and ICs were calculated using the interaction model and an external validation was performed. Doses adjustments were calculated in order to obtain equal values for drug exposure in extensive and poor metabolisers and then compared to Epar/Vidal dosing guidelines. RESULTS: CRs were assessed for 26 substrates, FAs for five genotype classes including CYP2C9*2 and *3 allelic variants, IRs for 27 inhibitors and ICs for two inducers. For the genetic model, the mean prediction error of AUC ratios was -0.01, while the mean prediction absolute error was 0.36. For the drug interaction model, the mean prediction error of AUC ratios was 0.01, while the mean prediction absolute error was 0.22. Of the 26 substrates and CYP2C9*2 and *3 variants investigated, 30 couples (substrate, genotype) lead to a dose adjustment, as opposed to only ten couples identified in the Epar/Vidal recommendations. CONCLUSION: These models were already used for CYP2D6. They are accurate at predicting the impact of drug interactions and genetic polymorphisms on CYP2C9 substrate exposure. This approach will contribute to the development of personalized medicine, i.e. individualized drug therapy with specific dosing recommendations based on CYP genotype or drug associations.

Experimental designs for small randomised clinical trials: an algorithm for choice.

BACKGROUND: Small clinical trials are necessary when there are difficulties in recruiting enough patients for conventional frequentist statistical analyses to provide an appropriate answer. These trials are often necessary for the study of rare diseases as well as specific study populations e.g. children. It has been estimated that there are between 6,000 and 8,000 rare diseases that cover a broad range of diseases and patients. In the European Union these diseases affect up to 30 million people, with about 50% of those affected being children. Therapies for treating these rare diseases need their efficacy and safety evaluated but due to the small number of potential trial participants, a standard randomised controlled trial is often not feasible. There are a number of alternative trial designs to the usual parallel group design, each of which offers specific advantages, but they also have specific limitations. Thus the choice of the most appropriate design is not simple. METHODS: PubMed was searched to identify publications about the characteristics of different trial designs that can be used in randomised, comparative small clinical trials. In addition, the contents tables from 11 journals were hand-searched. An algorithm was developed using decision nodes based on the characteristics of the identified trial designs. RESULTS: We identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the evaluation of therapies in orphan diseases. The main characteristics and the advantages and limitations of these designs were summarised and used to develop an algorithm that may be used to help select an appropriate design for a given clinical situation. We used examples from publications of given disease-treatment-outcome situations, in which the investigators had used a particular trial design, to illustrate the use of the algorithm for the identification of possible alternative designs. CONCLUSIONS: The algorithm that we propose could be a useful tool for the choice of an appropriate trial design in the development of orphan drugs for a given disease-treatment-outcome situation.