Estimation of Dosing Strategies for Individualisation

Detta är en avhandling från Uppsala : Acta Universitatis Upsaliensis

Sammanfattning: To increase the proportion of patients with successful drug treatment, dose individualisation on the basis of one or several patient characteristics, a priori individualisation, and/or on the basis of feedback observations from the patient following an initial dose, a posteriori individualisation, is an option. Efficient tools in optimising individualised dosing strategies are population models describing pharmacokinetics (PK) and the relation between pharmacokinetics and pharmacodynamics (PK/PD).Methods for estimating optimal dosing strategies, with a discrete number of doses, for dose individualisation a priori and a posteriori were developed and explored using simulated data. The methods required definitions of (i) the therapeutic target, i.e. the value of the target variable and a risk function quantifying the seriousness of deviation from the target, (ii) a population PK/PD model relating dose input to the target variable in the patients to be treated, and (iii) distributions of relevant patient factors. Optimal dosing strategies, in terms of dose sizes and individualisation conditions, were estimated by minimising the overall risk. Factors influencing the optimal dosing strategies were identified. Consideration of those will have implications for study design, data collection, population model development and target definition.A dosing strategy for a priori individualisation was estimated for NXY-059, a drug under development. Applying the estimated dosing strategy in a clinical study resulted in reasonable agreement between observed and expected outcome, supporting the developed methodology.Estimation of a dosing strategy for a posteriori individualisation for oxybutynin, a drug marketed for the treatment of overactive bladder, illustrated the implementation of the method when defining the therapeutic target in terms of utility and responder probability, that is, as a combination of the desired and adverse effects.The proposed approach provides an estimate of the maximal benefit expected from individualisation and, if individualisation is considered clinically superior, the optimal conditions for individualisation. The main application for the methods is in drug development where the methods can be generally employed in the establishment of dosing strategies for individualisation with relevant extensions regarding population model complexity and individualisation conditions.