A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials.

Background: The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally.

Objective: We aimed to optimize AD trial design using simulations.

Methods: We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens.

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma.

Background: Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data.

Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma.

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse.

A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab.

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin-like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)-rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism.

The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease.

The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study.

Improving the economics of NASH/NAFLD treatment through the use of systems biology.

Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease (NAFLD). We surveyed NASH therapies currently in development, and found a significant variety of targets and approaches. Evaluation and clinical testing of these targets is an expensive and time-consuming process.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis.

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s.