The simcyp population based simulator: architecture, implementation, and quality assurance

Abstract

Developing a user-friendly platform that can handle a vast number of complex physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models both for conventional small molecules and larger biologic drugs is a substantial challenge. Over the last decade the Simcyp Population Based Simulator has gained popularity in major pharmaceutical companies (70% of top 40 - in term of R&D spending). Under the Simcyp Consortium guidance, it has evolved from a simple drug-drug interaction tool to a sophisticated and comprehensive Model Based Drug Development (MBDD) platform that covers a broad range of applications spanning from early drug discovery to late drug development. This article provides an update on the latest architectural and implementation developments within the Simulator. Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described. The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes. This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.

Keywords: ADME; Model based drug development; Pharmacodynamics; Pharmacokinetics; Physiologically-based pharmacokinetic; Simcyp.

Figure 1

The chronology of expansion of the Simulator features from 2001–2013 under the Simcyp Consortium guidance. The development started with static metabolic drug-drug interaction calculations then dynamic drug-drug interaction models followed by whole body PBPK and so on.

Figure 2

The overall autotesting process which starts from running the repository of workspaces to the generation of summary reports.

Figure 3

A screen shot of the automated sensitivity analysis tool in Simcyp Version 12 Release 2; an example for assessing the impact of fraction unbound in plasma and the absorption rate constant on specific outputs where the minimum and maximum values, the steps and the step-size distributions are defined.

Figure 4

A screen shot of the Parameter Estimation (PE) module that allows either of simulation or estimation modes. The observed clinical data are loaded in XML format and in the shown case the data include both plasma concentration and a PD response profile for simultaneous fitting of PK and PD dependent variables.

Figure 5

Simcyp screen representing a PD link response unit: input from the previous unit can go through a Link transform model and then feed into either a growth/progression/turnover model, survival model or custom scripted model.