Robust parameter estimation for dynamical systems from outlier-corrupted data.

Motivation: Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier corruption of datasets often cannot be avoided. These outliers may distort the parameter estimates, resulting in incorrect model predictions. Robust parameter estimation methods are required which provide reliable parameter estimates in the presence of outliers. Results: In this manuscript, we propose and evaluate methods for estimating the parameters of ordinary differential equation models from outlier-corrupted data. As alternatives to the normal distribution as noise distribution, we consider the Laplace, the Huber, the Cauchy and the Student's t distribution. We assess accuracy, robustness and computational efficiency of estimators using these different distribution assumptions. To this end, we consider artificial data of a conversion process, as well as published experimental data for Epo-induced JAK/STAT signaling. We study how well the methods can compensate and discover artificially introduced outliers. Our evaluation reveals that using alternative distributions improves the robustness of parameter estimates. Availability and Implementation: The MATLAB implementation of the likelihood functions using the distribution assumptions is available at Bioinformatics online. Contact: jan.hasenauer@helmholtz-muenchen.de. Supplementary information: Supplementary material are available at Bioinformatics online.

Pro-Fibrotic Effects of CCL18 on Human Lung Fibroblasts Are Mediated via CCR6.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease of unknown origin, with a median patient survival time of ~3 years after diagnosis without anti-fibrotic therapy. It is characterized by progressive fibrosis indicated by increased collagen deposition and high numbers of fibroblasts in the lung. It has been demonstrated that CCL18 induces collagen and αSMA synthesis in fibroblasts. We aimed to identify the CCL18 receptor responsible for its pro-fibrotic activities. METHODS: We used a random phage display library to screen for potential CCL18-binding peptides, demonstrated its expression in human lungs and fibroblast lines by PCR and immunostaining and verified its function in cell lines. RESULTS: We identified CCR6 (CD196) as a CCL18 receptor and found its expression in fibrotic lung tissue and lung fibroblast lines derived from fibrotic lungs, but it was almost absent in control lines and tissue. CCL18 induced receptor internalization in a CCR6-overexpressing cell line. CCR6 blockade in primary human lung fibroblasts reduced CCL18-induced FGF2 release as well as collagen-1 and αSMA expression. Knockdown of CCR6 in a mouse fibroblast cell line abolished the induction of collagen and α-smooth muscle actin expression. CONCLUSION: Our data indicate that CCL18 triggers pro-fibrotic processes via CCR6, highlighting its role in fibrogenesis.

Cedirogant Population Pharmacokinetics and Pharmacodynamic Analyses of Interleukin-17A Inhibition in Two Phase 1 Studies in Healthy Participants and Participants with Moderate to Severe Psoriasis.

Cedirogant (ABBV-157) is an orally bioavailable inverse agonist of retinoic acid-related orphan receptor gamma thymus. Data from 2 Phase 1 studies were used to characterize cedirogant pharmacokinetics and evaluate target engagement. Cedirogant plasma concentrations and ex vivo interleukin 17A (IL-17A) concentrations from healthy participants and participants with moderate to severe psoriasis (PsO) were analyzed in a population pharmacokinetic and pharmacodynamic modeling framework to characterize cedirogant pharmacokinetics following single and multiple doses and assess ex vivo IL-17A inhibition in relation to cedirogant exposure. Cedirogant population pharmacokinetics were best described by a 2-compartment pharmacokinetic model with delayed absorption and an enzyme turnover compartment to describe cytochrome P450 3A autoinduction. The pharmacokinetics of cedirogant were comparable between healthy participants and participants with PsO. Cedirogant steady-state average and maximum plasma concentrations were predicted to be 7.56 and 11.8 mg/L, respectively, for participants with PsO for the 375 mg once-daily regimen on Day 14. The apparent clearance and apparent volume of distribution for cedirogant were estimated to be 24.5 L/day and 28.2 L, respectively. A direct maximum inhibition model adequately characterized the exposure-response relationship of cedirogant and ex vivo IL-17A inhibition, indicating no temporal delay between exposure and response with a saturable inhibition of IL-17A. Model-estimated half-maximal inhibitory concentration and maximum inhibition values for cedirogant inhibition of ex vivo IL-17A were 0.56 mg/L and 0.76, respectively. The established relationship between cedirogant exposure and biomarker effect supported dose selection for the Phase 2 dose-ranging study in patients with PsO.

A continued learning approach for model-informed precision dosing: Updating models in clinical practice.

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.

Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology.

Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dosing tables or are based on maximum a posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient-specific information. We propose three novel approaches for MIPD using Bayesian data assimilation (DA) and/or reinforcement learning (RL) to control neutropenia, the major dose-limiting side effect in anticancer chemotherapy. These approaches have the potential to substantially reduce the incidence of life-threatening grade 4 and subtherapeutic grade 0 neutropenia compared with existing approaches. We further show that RL allows to gain further insights by identifying patient factors that drive dose decisions. Due to its flexibility, the proposed combined DA-RL approach can easily be extended to integrate multiple end points or patient-reported outcomes, thereby promising important benefits for future personalized therapies.

Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy.

An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.