Model based dose personalization in clinical trials.

BACKGROUND AND OBJECTIVE: Personalized medicine is an important area of medical research which consists of designing therapies specifically for a patient or a group of patients. For drugs having a narrow therapeutic index or for vulnerable patients, methods such as therapeutic drug monitoring are used in a hospital setting to ensure that the blood concentration of the drug is maintained within a pre-decided range. However, such methods can not be used for drugs which are still in the developmental phase since, generally, insufficient information is available about the pharmacokinetic behaviour of the drug. METHODS: In this paper, we present a new methodology for explicit optimization of dose regimens during the course of the pharmacokinetic studies such that the resultant blood concentration of the drug in each subject is maintained around a desired target concentration or within a target range. RESULTS: We demonstrate that our algorithm is able to achieve the clinical objective of PK estimation while simultaneously individualizing the dose to every subject in the trial. Our algorithm computes dose regimens that, on average, have a relative efficiency of 97% with a standard deviation of less than 5%. The results show that the algorithm can be relied upon to ensure that the subjects in the trial are minimally over- and under-exposed to the test therapy. CONCLUSIONS: The proposed methodology can assist in ensuring correct dosing to each subject in a clinical trial so that each subject receives only the intended exposure to the drug while simultaneously estimating the PK profile of the drug. Our methodology can also be applied in randomized concentration-controlled trials where maintenance of the target concentration in the subjects is a fundamental requirement for conducting these trials.

Dose optimisation with simultaneous pharmacokinetic estimation in adaptive clinical trials.

Determination of the optimal dose is a critical objective in the drug developmental process. An optimal dose prevents over- and under-exposure to the treatment drug thereby facilitating superior patient experience and reduced costs to the healthcare system. In this paper, we present a method for model-based dose optimisation with simultaneous pharmacokinetic estimation of the model parameters. Multiple doses of the drug are considered and the objective is to maintain the blood concentration of the drug around a pre-decided target concentration. We consider an adaptive setting wherein the model parameters are estimated from the blood samples collected at D-optimal time points from all subjects enrolled so far in the trial. The estimated parameters are then used to determine the optimal dose regimen for the next cohort. This procedure continues until the condition of a pre-decided stopping rule is met. Simulation studies and sensitivity analysis are undertaken to validate the methodology. We also evaluate the performance of the methodology when carried out in a non-adaptive setting. A two-stage design is then presented which combines the advantages of the adaptive as well as the non-adaptive approach. We demonstrate that our methodology enables pharmacokinetic estimation and dose regimen optimisation simultaneously in an ethical and cost-effective manner protecting the subjects from the ill-effects of suboptimal dose regimens and economising the number of subjects required in the trial.

Optimizing dose regimens and fixed dose combination ratios in clinical trials.

Successful treatment of many diseases depends on the level of drug concentration in blood and its maintenance over a period of time around a value considered as therapeutic. The dose regimen that minimizes the underexposure and overexposure around the target concentration maximizes efficacy and safety, resulting in increased chances of a successful patient recovery. We present a method of computer-assisted dose finding by explicit optimization of a target criterion. We develop a general theory for such dose regimens and propose criteria for their computation. This approach is likely to supersede "brute force" techniques exclusively based on simulation. In case of a combination of two drugs in a single dosing unit, it is crucial that the optimal combination ratio is identified during the developmental process and is taken forward to further trials or approval. The algorithm computes the optimal ratio along with the optimal dose regimen. If the interest is in restricting the concentration profile of the drug to a therapeutic range, we adapt the algorithm to determine the optimal dose regimen. In future, this work is intended to aid the development of fixed dose combinations, especially antimalarials and other anti-infectives. The methodology also has potential applications in randomized concentration-controlled trials where adherence to the target concentration is a fundamental requirement.