FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective.

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

Towards optimal virtual patients: an online adaptive control approach.

The application of biosimulation to drug discovery and optimization is enhanced by applying in silico disease models that capture reported heterogeneity in patient clinical phenotypes. Using such a diverse cohort of virtual patients improves the robustness of the in silico analysis and allows critical hypothesis testing to explore key knowledge gaps. The rapid development of a diverse virtual patient cohort exhibiting appropriate steady-state and dynamic behaviors subject to a wide spectrum of stimuli is challenging due to the complexity of the mathematical representation of the biological system, rendering manual parameter tuning infeasible. In this paper, we present an online adaptive control technique, based on model reference adaptive control (MRAC), to optimally auto-tune model parameters for a virtual patient population in order to meet the desired stimulus-response constraints. We validate the efficacy of the control scheme on the Entelos Metabolism PhysioLab platform by automatically generating a cohort of validated virtual patients suitable for in silico research.

The virtual NOD mouse: applying predictive biosimulation to research in type 1 diabetes.

Type 1 diabetes is a complex, multifactorial disease characterized by T cell-mediated autoimmune destruction of insulin-secreting pancreatic beta cells. To facilitate research in type 1 diabetes, a large-scale dynamic mathematical model of the female non-obese diabetic (NOD) mouse was developed. In this model, termed the Entelos Type 1 Diabetes PhysioLab platform, virtual NOD mice are constructed by mathematically representing components of the immune system and islet beta cell physiology important for the pathogenesis of type 1 diabetes. This report describes the scope of the platform and illustrates some of its capabilities. Specifically, using two virtual NOD mice with either average or early diabetes-onset times, we demonstrate the reproducibility of experimentally observed dynamics involved in diabetes progression, therapeutic responses to exogenous IL-10, and heterogeneity in disease onset. Additionally, we use the Type 1 Diabetes PhysioLab platform to investigate the impact of disease heterogeneity on the effectiveness of exogenous IL-10 therapy to prevent diabetes onset. Results indicate that the inability of a previously published IL-10 therapy protocol to protect NOD mice who exhibit early diabetes onset is due to high levels of pancreatic lymph node (PLN) inflammation, islet infiltration, and beta cell destruction at the time of treatment initiation. Further, simulation indicates that earlier administration of the treatment protocol can prevent NOD mice from developing diabetes by initiating treatment during the period when the disease is still sensitive to IL-10's protective function.

Dosing and timing effects of anti-CD40L therapy: predictions from a mathematical model of type 1 diabetes.

Several publications describing the use of anti-CD40L monoclonal antibodies (anti-CD40L) for the treatment of type 1 diabetes in non-obese diabetic (NOD) mice have reported different treatment responses to similar protocols. The Entelos Type 1 Diabetes PhysioLab platform, a dynamic large-scale mathematical model of the pathogenesis of type 1 diabetes, was used to study the effects of anti-CD40L therapy in silico. An examination of the impact of pharmacokinetic variability and the heterogeneity of disease progression rate on therapeutic outcome provided insights that could reconcile the apparently conflicting data. Optimal treatment protocols were identified by exploring the dynamics of key pathophysiological pathways.

Mechanisms mediating anti-CD3 antibody efficacy: insights from a mathematical model of type 1 diabetes.

Anti-CD3 antibody therapy, a promising clinical approach for the treatment of type 1 diabetes (T1D), was investigated using a mathematical model of T1D in the female nonobese diabetic (NOD) mouse. Analyses of model simulation results indicate that, in addition to the known direct effects of anti-CD3 antibody on T lymphocytes, two additional mechanisms are required for sustained disease remission: (a) rapid regrowth of healthy beta cells following clearance of islet inflammation and (b) enhanced regulatory T cell activity and/or phenotypic changes in antigen presenting cells (APCs) that promote a stable regulatory environment in the pancreas.

Iterative approach to model identification of biological networks.

BACKGROUND: Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using available system knowledge and experimental measurements. RESULTS: The scheme includes a state regulator algorithm that provides estimates of all system unknowns (concentrations of the system components and the reaction rates of their inter-conversion). The full system information is used for estimation of the model parameters. An optimal experiment design using the parameter identifiability and D-optimality criteria is formulated to provide "rich" experimental data for maximizing the accuracy of the parameter estimates in subsequent iterations. The importance of model identifiability tests for optimal measurement selection is also considered. The iterative scheme is tested on a model for the caspase function in apoptosis where it is demonstrated that model accuracy improves with each iteration. Optimal experiment design was determined to be critical for model identification. CONCLUSION: The proposed algorithm has general application to modeling a wide range of cellular processes, which include gene regulation networks, signal transduction and metabolic networks.

Estimating optimal profiles of genetic alterations using constraint-based models.

Metabolic engineering involves application of recombinant DNA methods to manipulate metabolic networks to improve cellular properties. It is critical that the genetic alterations be performed in an optimal manner to maximize profit. In addition to the product yield, productivity consideration is also critical, especially for the production of bulk chemicals such as 1,3-propanediol. In this work, we demonstrate that it is suboptimal from the standpoint of productivity to induce genetic alteration at the start of the production process. A bi-level optimization scheme is formulated to determine the optimal temporal flux profile for the manipulated reaction. In the first case study, an optimal flux in the reaction catalyzed by glycerol kinase is determined to maximize the glycerol production at the end of a 6-h batch cultivation of Escherichia coli under aerobic conditions. The final glycerol concentration is 30% higher for the optimal flux profile compared with having an active flux during the entire batch. The effect of the mass transfer coefficient on the optimal profile and the glycerol concentration is also determined. In the second case study, the anaerobic batch fermentation of the ldh(-) strain of Escherichia coli is considered. The optimal flux in the acetate pathway is determined to maximize the final ethanol concentration. The optimal flux results in higher ethanol concentration (11.92 mmol L(-1)) compared to strains with no acetate flux (8.36 mmol L(-1)) and fully active acetate flux (6.22 mmol L(-1)). We also examine the effects of growth inhibition due to high ethanol concentrations and variations in final batch time on ethanol production.

Cybernetic model predictive control of a continuous bioreactor with cell recycle.

The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.