Background: Inclisiran, an siRNA targeting PCSK9 mRNA, administered twice-yearly, substantially and sustainably reduced LDL-C. Data quantifying how lowering LDL-C with inclisiran translates into a reduced risk of major adverse cardiovascular events (MACE) and comparison to available lipid-lowering therapies are lacking. In silico trials applying a disease computational model to virtual patients receiving various treatments allow such predictions in large scale clinical trials. Purpose: The aim of the in silico SIRIUS trial (NCT05974345) was to predict the effect of inclisiran compared to ezetimibe or evolocumab on MACE in participants with atherosclerotic cardiovascular disease (ASCVD). Methods: Two distinct in silico trials were conducted using knowledge-based mechanistic computational model of ASCVD applied to virtual populations (Vpop) with ASCVD and LDL-C ≥ 70 mg/dL, each virtual patient being its own control. This model was previously calibrated to reproduce the results of FOURIER and ORION-10 trials and validated based on the results of other trials results including ODYSSEY-OUTCOMES. The first trial compared inclisiran efficacy to ezetimibe as an adjunct to high-intensity (HI) statins, while the second one compared inclisiran efficacy to evolocumab as adjunct to HI statin and ezetimibe. In both trials, the main outcome was a 3-Point-MACE defined as a composite of time to first occurrence of cardiovascular (CV) death, nonfatal myocardial infarction or nonfatal ischemic stroke over 5 years. Results: In a Vpop of 162,453 ASCVD patients under HI statin, the difference in mean percentage reduction in LDL-C with inclisiran as compared to ezetimibe was 32.7% from a median baseline value of 91.1 mg/dL to 48.3 mg/dL and 77.2 mg/dL at 5 years. Inclisiran was more effective than ezetimibe to lower the risk of 3P-MACE (11.3 vs. 13.3%; HR 0.82, low degree of prediction uncertainty). In a Vpop of 42,238 ASCVD patients under HI statin and ezetimibe, similar mean percentage reduction in LDL-C with inclisiran as compared to evolocumab was achieved at 5 years. Inclisiran and evolocumab had similar efficacy on lowering the risk of 3P-MACE (12.5 vs. 12.4%; HR 1.01, medium degree of prediction uncertainty). Conclusions: These in silico trials provide early insights into the potential effect of inclisiran on ASCVD events compared to alternative treatments suggesting a higher 3P-MACE reduction compared to ezetimibe and a similar effect compared to evolocumab.

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. The SIRIUS in-silico trial programme aims to predict the efficacy of inclisiran on CV events in individuals with established atherosclerotic cardiovascular disease (ASCVD). METHODS AND RESULTS:A knowledge-based mechanistic model of ASCVD was built, calibrated, and validated to conduct the SIRIUS programme (NCT05974345) aiming to predict the effect of inclisiran on CV outcomes. The SIRIUS Virtual Population included patients with established ASCVD (previous myocardial infarction (MI), previous ischemic stroke (IS), previous symptomatic lower limb peripheral arterial disease (PAD) defined as either intermittent claudication with ankle-brachial index <0.85, prior peripheral arterial revascularization procedure, or vascular amputation) and fasting LDL-C ≥ 70 mg/dL, despite stable (≥4 weeks) well-tolerated lipid-lowering therapies.SIRIUS is an in-silico multi-arm trial programme. It follows an idealized crossover design where each virtual patient is its own control, comparing inclisiran to (i) placebo as adjunct to high-intensity statin therapy with or without ezetimibe, (ii) ezetimibe as adjunct to high-intensity statin therapy, (iii) evolocumab as adjunct to high-intensity statin therapy and ezetimibe.The co-primary efficacy outcomes are based on the time to the first occurrence of any component of 3P-MACE (composite of CV death, nonfatal MI, or nonfatal IS) and time to occurrence of CV death over 5 years. PERSPECTIVES/CONCLUSION:The SIRIUS in-silico trial programme will provide early insights regarding potential effect of inclisiran on MACE in ASCVD patients, several years before the availability of the results from ongoing CV outcomes trials (ORION-4 and VICTORION-2-P). CLINICAL TRIAL REGISTRATION:Clinicaltrials.gov identifier: NCT05974345.

Introduction: Symptomatic lower extremity peripheral artery disease (PAD) patients have the highest risk of cardiovascular (CV) outcomes among patients with established atherosclerotic cardiovascular disease (ASCVD). Inclisiran, an siRNA targeting PCSK9 mRNA, reduces LDL-C levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower CV events in ASCVD patients. Research question/Hypothesis: SIRIUS in silico study aims to predict the efficacy of inclisiran on CV outcomes in subgroups of patients with or without PAD. Methods: The SIRIUS in silico trial was conducted using a knowledge-based mechanistic computational model of ASCVD applied to a virtual ASCVD population with LDL-C ≥ 70 mg/dL. Each virtual patient is its own control. This model was previously calibrated and validated before running the trial. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statins with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal ischemic stroke (IS) over 5 years. Occurrence of major acute limb events (MALE) was also individually assessed in time-to-first-event analyses. Results: Among 204, 691 virtual SIRIUS ASCVD patients, 28,072 (13.7%) had PAD. At 5 years, the mean predicted percentage reduction in LDL-C with inclisiran as compared to placebo was -49.3% and -49.8% in patients with or without PAD, respectively. The predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm in patients with PAD (17.62% vs 22.88%; Hazard Ratio (HR): 0.75 medium uncertainty) and in patients without PAD (10.33% vs 13.64%; HR: 0.75 low uncertainty). Compared to placebo, inclisiran was also predicted to consistently reduce MALE in patients with or without PAD (2.71% vs 4.11%; HR: 0.65 medium uncertainty and 0.19% vs 0.29%; HR: 0.66 high uncertainty respectively). Conclusion: Pending the results of ORION-4 and VICTORION-2-P, this first In Silico trial in virtual PAD patients predicted a potential effect of inclisiran on 3P-MACE and MALE reductions over 5 years follow-up.

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. The model accounts for cross-reactivity of immune cells elicited during previous immunizations with new antigens. We simulated vaccine effectiveness (sVE) of high dose (HD) versus standard dose (SD) vaccines in the older population, from 2011 to 2022. We find that sVE is highly dependent on antigenic matching and that higher dosage improves immunogenicity, activation and memory formation of immune cells. In alignment with clinical observations, the HD vaccine performs better than the SD vaccine in all simulations, supporting the use of the HD vaccine in the older population. This model could be adapted to predict the impact of alternative virus strain selection on clinical outcomes in future influenza seasons.

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. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

Introduction: Inclisiran, an siRNA, targeting PCSK9 mRNA, reduces LDL-c levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower cardiovascular (CV) events in virtual patients with atherosclerotic cardiovascular disease (ASCVD). Research question: This analysis predicted the potential efficacy of inclisiran on CV outcomes in virtual patients with or without prior ischemic stroke (IS). Methods: The SIRIUS trial was conducted using a calibrated and validated knowledge-based mechanistic computational model of ASCVD applied to a virtual population with LDL-C ≥ 70 mg/dL. Each virtual patient was its own control. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statin with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal IS over 5 years in patients with or without prior IS. Occurrence of fatal and non-fatal (IS) was also individually assessed in time-to-first-event analyses. Results: Among 204,691 virtual SIRIUS ASCVD patients, 39 371 (19%) had prior IS. At 5 years, the predicted mean percentage reduction in LDL-C with inclisiran as compared to placebo was –49.17% and –49.88% in patients with or without prior IS respectively. Patients with prior IS were at higher risk of 3P-MACE than patients without IS both with placebo and inclisiran (17.01% vs 14.41% with placebo and 13.44% vs 10.83% with inclisiran). However, the predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm for both prior IS and no prior IS (HR 0.78 medium uncertainty and 0.74 low uncertainty respectively). Compared to placebo, inclisiran was also predicted to consistently reduce fatal and non-fatal IS in patients with or without prior IS (5.45% vs 7.22%; HR: 0.75 medium uncertainty and 1.87% vs 2.54%; HR: 0.73 medium uncertainty respectively). Conclusion: SIRIUS provides insights into the potential efficacy of inclisiran on CV events suggesting a substantial 3P-MACE and fatal and non-fatal IS reduction in ASCVD patients including those with prior IS, several years before the availability of results from ongoing outcomes trials (ORION-4, VICTORION-2P).

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. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

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. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

The term " In Silico Trial" indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative.

The added value of in silico models (including quantitative systems pharmacology models) for drug development is now unanimously recognized. It is, therefore, important that the standards used are commonly acknowledged by all the parties involved. On April 25 and 26, 2019, a multistakeholder workshop on the validation challenges for in silico models in drug development was organized in Belgium. As an outcome, a White Paper is foreseen in 2020 on standards for in silico model verification and validation. Drug research, design, and development has a long-standing tradition in the use of in silico methodologies. In the context of clinical drug development Quantitative Structure-Property Relationship models in general and Quantitative Structure-Activity Relationship (QSAR) methods in particular, as well as pharmacometric approaches like population pharmacokinetics, pharmacokinetics (PKs)/pharmacodynamics, exposure-response, and physiology-based pharmacokinetics (PBPK) models are well-known. However, the in silico toolbox is rapidly expanding beyond these traditional/historical modeling technologies and new ones have emerged the last decades, including multiphysics simulations, the so-called systems medicine/pharmacology models (QSP) and clinical trial simulation tools (in silico clinical trials). In the remainder of this document, the term in silico models will be used to describe the collection of all the aforementioned modeling technologies. The added value of in silico models for drug development is now unanimously recognized by the scientific community.1, 2 Irrespective of the model used and the concerned part of the drug development pipeline, the evidence generated from these models, also called digital evidence, might eventually be included in regulatory submissions. In that case, the incorporation of digital evidence needs to follow standards of data/evidence generation, analysis, and reporting to enable the regulatory bodies to efficiently perform an adequate assessment of the submitted material. It is, therefore, of utmost importance that the standards to be considered are commonly acknowledged by all the involved parties (regulators, health technology assessment (HTA) agencies, academia, industry, regulators, and patients) and are relevant for all the types of models that can be included in regulatory submissions. The endorsement of these standards by regulators is particularly valuable because regulators generally provide guidance for data generation and reporting back to sponsors (industry or academia) thereby accelerating the uptake of the standards in the entire community and in the healthcare systems. Specific guidance documents on the reporting, verification, and validation of in silico models (including QSP models) for drug development/approval are, therefore, currently an unmet growing need. One of the prerequisites for the development of such regulatory guidance documents, in addition to some skills and experience from the concerned assessors, is the agreement on standards among relevant aforementioned stakeholders and further described hereafter. Of interest is the standard recently published by the American Society of Mechanical Engineers (ASME) on assessment of credibility of computational modeling through Verification and Validation, applied to medical devices (V&V40).9 The application of this framework to PBPK modeling was published the same year.10 In the current situation, a similar initiative oriented to drug development exceeding PBPK would be of great value. Lessons learned from regulatory guidelines on QSAR and traditional pharmacometric models reflect the general philosophy that model evaluation starts with the regulatory impact assessment closely related to the context of use.5-10 Two important points should be considered: (i) what the impact is of the model prediction on the identification of the appropriate research and development strategy and (ii) what the impact is of the research and development strategy in the regulatory submission. If both impacts are rated as high (e.g., model predictions used to replace a therapeutic study for extension of an indication in children), the requirements regarding overall quality of the model and related data are much more stringent than if both impacts are rated low (e.g., population pharmacokinetic model to describe data from a well-designed phase I PK study). Moreover, for in silico models, good tracking and adequate reporting of knowledge and data sources, analytical and statistical tools, as well as decision criteria to move to the next step/component, or to assess the whole model should also be part of such guidance documents. In view of the currently unmet need for specific guidance and complexity of the task, tackling the validation challenge of the growing amount of digital evidence is, therefore, not something that any stakeholder should be left alone with, be it the regulators, academia, industry, patients, payers, HTA agencies, or healthcare professionals. On April 25 and 26, 2019, a workshop was organized in Belgium gathering regulators, academics, and industry to start working on tackling the validation challenge for in silico models in drug development. This successful meeting clearly showed common interest of the participating stakeholders. In a next phase, started September 2019, the initiative was extended to a larger number of stakeholders from the entire European Union (as detailed below) interested in this transdisciplinary inter-stakeholder project, aiming to provide a roadmap document (White Paper) on standards for assessment of in silico models dedicated to regulatory submission. This White Paper will discuss in detail all the gaps and challenges for in silico models verification and validation as well as the proposed approaches for moving forward illustrated by examples. They act as policymakers regarding drug assessment and need to ensure not only that suboptimal models are not being used for decision making but also that good and innovative models are not disregarded, all in the interest of public health. Adequate standards are, therefore, needed by regulators to make proper and consistent assessments in order to play their roles as both gatekeepers and enablers. Given the rapidly evolving field, the training of regulatory experts is made easier when clear standards and related up-to-date guidance documents are available. HTA agencies, as regulators, need clear standards and related up-to-date guidance documents to ensure a correct assessment of the novel drugs developed with the support of in silico models. One of the main drivers of innovation, academia, is regrettably not visible enough in the current scene of drug development or evaluation, if not under the umbrella of industry (as external consultants) or regulatory agencies (as external experts). By being part of the reflection on adequate standards for in silico modeling and by adopting these rules (and related terminology), it can be expected that the distance between academia and industry/regulators/patients can be narrowed. Furthermore, academia is the main producer of the data and knowledge on which knowledge-based models are built. The quality of this production needs to be improved, as shown by the reproducibility crisis,10 and verified if incorporated in a model. Without hampering innovation and flexibility inherent to academic research, the developed set of guidelines for verification and validation can also be applied to research models published by academia. Altogether, this will increase the robustness and repeatability of the published body of work and will align methodologies among all the stakeholders of tomorrow. Being the current key players for data and related model generation for drug development, it is essential that the industry is involved in the reflection to ensure that the proposed standards are realistic and implementable in practice. The transparency on the criteria and standards on which the produced models would be assessed by the regulator will permit better design and conduct of in-house modeling related activities and ultimately saving time and resources toward marketing of drugs. Having verification and validation guidelines means that in silico models can more readily be used by sponsors, and, per se, this will allow quicker and safer delivery of products to patients. Specifically, for niche populations (pediatrics and rare diseases), in silico might be the only way to obtain sufficient evidence to make rational decisions. In all domains, it should result in less patients enrolled in failed development as well as in successful ones. Despite the unanimous recognition of the added value of the in silico models for drug development, including systems medicine/pharmacology models and clinical trial simulations tools, the availability of specific guidance documents related to these models is currently an unmet growing need. There is an ongoing initiative in the European Union space, bringing together relevant stakeholders (academia, industry, and regulators) to agree on standards for assessment of these in silico models that will be considered as a premise of dedicated regulatory guidelines. A White Paper is planned for early 2020. F.T. Musuamba received funding from European commission: CSA EUSTAND4PM. The authors declared no competing interests for this work.