A new, second-order indirect model of depression time course

Objectives: The limited understanding of placebo effect and drug action in psychiatric diseases has led to a widespread use of ad-hoc empirical models [1,2] and simple indirect response models [3,4] to describe the time course of clinical scores (e.g. HAMD, PANSS) in psychiatric trials. Open issues include the ability to describe complex response profiles and the handling of different dosing schedules. This motivates the present work, where a new approach inspired by indirect response modelling is proposed. Methods: A new, second-order indirect model was devised in order to capture the structural properties of the treatment response (initial improvement followed by relapse). We extended the methodology of indirect response modelling to incorporate a feedback mechanism [5]. The model includes a compartment representing the HAMD score, with zero-order response formation (k_in) and first-order dissipation (k_out). Decrease of the HAMD state causes a stimulation of the response rate and therefore a feedback action. Treatment effect was modelled as an inhibitory function on the response rate. The proposed model was applied to two Phase II randomized, double-blind, placebo-controlled trials relative to a GlaxoSmithKline investigational antidepressant. Both studies featured a flexible dosing scheme that allowed non-responding patients to be escalated to a higher dose level. Parameter identification was performed with NONMEM 6.2 [6]. Results: The proposed model was successfully fitted to data of both studies. Individual data were well described. In particular, the new second-order indirect model was able to capture different patterns of response profiles, e.g. patients who improve steadily, non-responders, or patients who relapse into a depressive state after an initial improvement. Additionally, the model was able to describe changes in the response time course due to dose escalations. Visual predictive checks confirmed a proper characterization of the population distribution. Conclusions: Our results show the feasibility of a new modelling approach for longitudinal psychiatric data. In this work, we extended the well-known methodology of indirect response models to account for the complex patterns of response usually observed in psychiatric trials. This approach represents a step forward with respect to simple empirical models: the greater level of structure of the proposed model allows to describe complex response profiles and to perform simulations with different dosing schedules. References: [1] E.Y. Shang, M.A. Gibbs, J.W. Landen et al. (2009). Evaluation of structural models to describe the effect of placebo upon the time course of major depressive disorder. J Pharmacokinet Pharmacodyn 36:63-80. [2] G. Nucci, R. Gomeni, I. Poggesi (2009). Model-based approaches to increase efficiency of drug development in schizophrenia: a can’t miss opportunity. Expert Opin Drug Discov 4:837-856. [3] D.E. Mager, E. Wyska, W.J. Jusko (2003). Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos 31:510–519. [4] I. Ortega Azpitarte, A. Vermeulen, V. Piotrovsky (2006). Concentration-response analysis of antipsychotic drug effects using an indirect response model. Population Approach Group in Europe 15th Meeting. [5] K.P. Zuideveld, H.J. Maas, N. Treijtel et al. (2001). A set-point model with oscillatory behavior predicts the time course of 8-OH-DPAT-induced hypothermia. Am J Physiol Regulatory Integrative Comp Physiol 281:R2059–R2071. [6] Beal, S.L., Sheiner, L.B., Boeckmann, A.J. (Eds.), 1989–2006. NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA.