Explore the vast range of titles available.

Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)—a set of computational methods addressing optimization problems as a continuous learning process—shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry—a way to characterize mental dysfunctions in terms of aberrant brain computations—and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.

Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state‐of‐the‐art tools used to inform dosing in drug development. In particular, we used pharmacokinetic‐pharmacodynamic (PK‐PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be tested experimentally. Through simulations, we show that, when learning from retrospective trial data, more than 100 patients are needed to reach an accuracy within the range of what is achieved with a standard dosing solution. However, embedding a model of drug effect within the RL algorithm improves accuracy by reducing errors to target by 90% through learning to take dosing actions maximizing long‐term benefit. Data residual variability impacts accuracy while the algorithm efficiently coped with up to 50% interindividual variability in the PK and 25% in the PD model's parameters. We illustrate how extending the state definition of the RL agent with meaningful variables is key to achieve high accuracy of optimal dosing policy. These results suggest that RL constitutes an attractive approach for precision dosing when rich data are available or when complemented with synthetic data from model‐based tools used in model‐informed drug development.

To support further development of model-informed drug development approaches leveraging circulating tumor DNA (ctDNA), we performed an exploratory analysis of the relationships between treatment-induced changes to ctDNA levels, clinical response and tumor size dynamics in patients with cancer treated with checkpoint inhibitors and targeted therapies. This analysis highlights opportunities for pharmacometrics approaches such as for optimizing sampling design strategies. It also highlights challenges related to the nature of the data and associated variability overall emphasizing the importance of mechanistic modeling studies of the underlying biology of ctDNA processes such as shedding, release and clearance and their relationships with tumor size dynamic and treatment effects.

Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.

Purpose For nonlinear mixed-effects pharmacometric models, diagnostic approaches often rely on individual parameters, also called empirical Bayes estimates (EBEs), estimated through maximizing conditional distributions. When individual data are sparse, the distribution of EBEs can “shrink” towards the same population value, and as a direct consequence, resulting diagnostics can be misleading. Methods Instead of maximizing each individual conditional distribution of individual parameters, we propose to randomly sample them in order to obtain values better spread out over the marginal distribution of individual parameters. Results We evaluated, through diagnostic plots and statistical tests, hypothesis related to the distribution of the individual parameters and show that the proposed method leads to more reliable results than using the EBEs. In particular, diagnostic plots are more meaningful, the rate of type I error is correctly controlled and its power increases when the degree of misspecification increases. An application to the warfarin pharmacokinetic data confirms the interest of the approach for practical applications. Conclusions The proposed method should be implemented to complement EBEs-based approach for increasing the performance of model diagnosis.

[...]as the immune status of a patient or comedications change, a drug that had not appeared immunogenic for many years of treatment could begin to induce an immune response. [...]a marketed drug may exhibit IG for the first time in a new and sensitive target population, such as patients with an autoimmune disease or children. [...]bioinformatic approaches provide a good basis for screening and optimizing compounds, but they cannot be used to manage IG once a protein therapeutic has entered human trials. The most frequent application of PBPK is the prediction of DDIs and the confidence in this approach is such that regulators accept simulations as a substitute for clinical trials and as the basis for label statements. [...]although DDIs still cannot be “engineered out” completely, they can be predicted and managed effectively through virtual trial simulation using models with sufficient mechanistic detail. The QSP model used in the IG Simulator has sufficient mechanistic detail to integrate diverse inputs, including bioinformatics predictions of MHC II binding to antigenic peptides, in vitro cell‐based assays and clinical measurements of compound concentrations, and ADA titers. [...]a detailed simulation of complex immune system interactions allows for

We present a nonlinear model of the dynamics of a cell population divided into proliferative and quiescent compartments. The proliferative phase represents the complete cell cycle ( G 1 − S − G 2 − M ) of a population committed to divide at its end. The model is structured by the time spent by a cell in the proliferative phase, and by the amount of Cyclin D /( CDK4 or 6 ) complexes. Cells can transit from one compartment to the other, following transition rules which differ according to the tissue state: healthy or tumoral. The asymptotic behaviour of solutions of the nonlinear model is analysed in two cases, exhibiting tissue homeostasis or tumour exponential growth. The model is simulated and its analytic predictions are confirmed numerically.

The Phase II trial of Anti-alpha-Synuclein Antibody in Early Parkinson’s Disease (PASADENA) is an ongoing double-blind, placebo-controlled trial evaluating the safety and efficacy of prasinezumab in early-stage Parkinson’s disease (PD). During the double-blind period, prasinezumab-treated individuals showed less progression of motor signs (Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS–UPDRS) Part III) than placebo-treated individuals. We evaluated whether the effect of prasinezumab on motor progression, assessed as a change in MDS–UPDRS Part III score in the OFF and ON states, and MDS–UPDRS Part II score, was sustained for 4 years from the start of the trial. We compared participants enrolled in the PASADENA open-label extension study with those enrolled in an external comparator arm derived from the Parkinson’s Progression Markers Initiative observational study. The PASADENA delayed-start ( n  = 94) and early-start ( n  = 177) groups showed a slower decline (a smaller increase in score) in MDS–UPDRS Part III scores in the OFF state (delayed start, −51%; early start, −65%), ON state (delayed start, −94%; early start, −118%) and MDS–UPDRS Part II (delayed start, −48%; early start, −40%) than did the Parkinson’s Progression Markers Initiative external comparator ( n  = 303). This exploratory analysis, which requires confirmation in future studies, suggested that the effect of prasinezumab in slowing motor progression in PD may be sustained long term. PASADENA ClinicalTrials.gov no. NCT03100149 . Individuals with Parkinson’s disease treated with prasinezumab demonstrated slower motor progression over 4 years compared with an external comparator cohort derived from the Parkinson’s Progression Markers Initiative observational study.

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.