Multi-strain modeling of influenza vaccine effectiveness in older adults and its dependence on antigenic distance.

Influenza vaccine effectiveness (VE) varies seasonally due to host, virus and vaccine characteristics. To investigate how antigenic matching and dosage impact VE, we developed a mechanistic knowledge-based mathematical model. Immunization with a split vaccine is modeled for exposure to A/H1N1 or A/H3N2 virus strains.

In-silico trial emulation to predict the cardiovascular protection of new lipid-lowering drugs: an illustration through the design of the SIRIUS programme.

Introduction: Inclisiran, an siRNA targeting hepatic PCSK9 mRNA, administered twice-yearly (after initial and 3-month doses), substantially and sustainably reduced LDL-cholesterol (LDL-C) in Phase III trials. Whether lowering LDL-C with inclisiran translates into a reduced risk of major adverse cardiovascular events (MACE) is not yet established. In-silico trials applying a disease computational model to virtual patients receiving new treatments allow to emulate large scale long-term clinical trials.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials.

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.

Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions.

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps-impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement.

Using numerical modeling and simulation to assess the ethical burden in clinical trials and how it relates to the proportion of responders in a trial sample.

In order to propose a more precise definition and explore how to reduce ethical losses in randomized controlled clinical trials (RCTs), we set out to identify trial participants who do not contribute to demonstrating that the treatment in the experimental arm is superior to that in the control arm. RCTs emerged mid-last century as the gold standard for assessing efficacy, becoming the cornerstone of the value of new therapies, yet their ethical grounds are a matter of debate. We introduce the concept of unnecessary participants in RCTs, the sum of non-informative participants and non-responders.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation.

From Clinical Trial Efficacy to Real-Life Effectiveness: Why Conventional Metrics do not Work.

Background: Randomised, double-blind, clinical trial methodology minimises bias in the measurement of treatment efficacy. However, most phase III trials in non-orphan diseases do not include individuals from the population to whom efficacy findings will be applied in the real world. Thus, a translation process must be used to infer effectiveness for these populations.

Chronic Lung Allograft Dysfunction: A Systematic Review of Mechanisms.

Chronic lung allograft dysfunction (CLAD) is the major limitation of long-term survival after lung transplantation. Chronic lung allograft dysfunction manifests as bronchiolitis obliterans syndrome or the recently described restrictive allograft syndrome. Although numerous risk factors have been identified so far, the physiopathological mechanisms of CLAD remain poorly understood.

High Risk versus Proportional Benefit: Modelling Equitable Strategies in Cardiovascular Prevention.

Objective: To examine the performances of an alternative strategy to decide initiating BP-lowering drugs called Proportional Benefit (PB). It selects candidates addressing the inequity induced by the high-risk approach since it distributes the gains proportionally to the burden of disease by genders and ages.

Study Design And Setting: Mild hypertensives from a Realistic Virtual Population by genders and 10-year age classes (range 35-64 years) received simulated treatment over 10 years according to the PB strategy or the 2007 ESH/ESC guidelines (ESH/ESC).

Comparative transcriptomic analysis between an artificially induced SIRS in healthy individuals and spontaneous sepsis.

Sepsis is defined as a syndrome combining a systemic inflammatory response with a documented infection. It may progress to more serious cases such as septic shock following the failure of one or more organs and the emergence of hemodynamic defects. Assuming that the emergence of serious septic syndromes may be partially explained by the early loss of regulation of the inflammatory response, we decided to compare, in a transcriptomic perspective, the biological mechanisms expressed during an induced systemic inflammatory response with those expressed during severe septic syndromes.

An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes.

Target identification aims at identifying biomolecules whose function should be therapeutically altered to cure the considered pathology. An algorithm for in silico target identification using Boolean network attractors is proposed. It assumes that attractors correspond to phenotypes produced by the modeled biological network.

Comparison of an effect-model-law-based method versus traditional clinical practice guidelines for optimal treatment decision-making: application to statin treatment in the French population.

Healthcare authorities make difficult decisions about how to spend limited budgets for interventions that guarantee the best cost-efficacy ratio. We propose a novel approach for treatment decision-making, OMES-in French: Objectif thérapeutique Modèle Effet Seuil (in English: Therapeutic Objective-Threshold-Effect Model; TOTEM). This approach takes into consideration results from clinical trials, adjusted for the patients' characteristics in treatment decision-making.

Better defining target populations for drugs with a view to reimbursement.

Reimbursement of drugs by public or private insurance systems is increasingly problematic, including in supposedly "rich" countries. There is an international consensus to consider the benefit of a Health technology assessment to clarify decisions on reimbursement by the collectivity, and this includes taking account of the target population of the new drug. The authors debate about the urgent need of a better quantification of the target population, which must include a qualitative description of this target population and a scientific extrapolation of the target population, which is certainly the most challenging problem.

Effect model law: an approach for the implementation of personalized medicine.

The effect model law states that a natural relationship exists between the frequency (observation) or the probability (prediction) of a morbid event without any treatment and the frequency or probability of the same event with a treatment. This relationship is called the effect model. It applies to a single individual, individuals within a population, or groups.

Reappraisal of metformin efficacy in the treatment of type 2 diabetes: a meta-analysis of randomised controlled trials.

Background: The UK Prospective Diabetes Study showed that metformin decreases mortality compared to diet alone in overweight patients with type 2 diabetes mellitus. Since then, it has been the first-line treatment in overweight patients with type 2 diabetes. However, metformin-sulphonylurea bitherapy may increase mortality.

Towards personalized medicine: exploring the consequences of the effect model-based approach.

Although personalized medicine has been a subject of research and debate in recent years, it has been underused in medical practice, except in some cancers. We believe that the main reason for the gap between the potential of personalized medicine and its use in daily medical practice can be explained by the lack of an appropriate tool to facilitate the use of biomarker values in a doctor's decision-making process. We propose that the effect model could form the basis of such a tool.

The global risk approach should be better applied in French hypertensive patients: a comparison between simulation and observation studies.

Background: The prediction of the public health impact of a preventive strategy provides valuable support for decision-making. International guidelines for hypertension management have introduced the level of absolute cardiovascular risk in the definition of the treatment target population. The public health impact of implementing such a recommendation has not been measured.

In silico study of the influence of intensity and duration of blood flow reduction on cell death through necrosis or apoptosis during acute ischemic stroke.

Ischemic stroke involves numerous and complex pathophysiological mechanisms including blood flow reduction, ionic exchanges, spreading depressions and cell death through necrosis or apoptosis. We used a mathematical model based on these phenomena to study the influences of intensity and duration of ischemia on the final size of the infarcted area. This model relies on a set of ordinary and partial differential equations.

SCORE should be preferred to Framingham to predict cardiovascular death in French population.

Background: Numerous studies have examined the validity of available scores to predict the absolute cardiovascular risk.

Design: We developed a virtual population based on data representative of the French population and compared the performances of the two most popular risk equations to predict cardiovascular death: Framingham and SCORE.

Methods: A population was built based on official French demographic statistics and summarized data from representative observational studies.

Exploration of beneficial and deleterious effects of inflammation in stroke: dynamics of inflammation cells.

The inflammatory process during stroke consists of activation of resident brain microglia and recruitment of leucocytes, namely neutrophils and monocytes/macrophages. During inflammation, microglial cells, neutrophils and macrophages secrete inflammatory cytokines and chemokines, and phagocytize dead cells. The recruitment of blood cells (neutrophils and macrophages) is mediated by the leucocyte-endothelium interactions and more specifically by cell adhesion molecules.

A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy.

Tumor angiogenesis is the process by which new blood vessels are formed and enhance the oxygenation and growth of tumors. As angiogenesis is recognized as being a critical event in cancer development, considerable efforts have been made to identify inhibitors of this process. Cytostatic treatments that target the molecular events of the angiogenesis process have been developed, and have met with some success.