In silico clinical trials: concepts and early adoptions

How Patients' Lifestyle Affects the Wear of Hip Implants: An In-Silico Study

The validation of new hip replacement designs traditionally relies on lengthy and costly In-Vitro wear tests that replicate basic In-Vivo conditions, as the simplified walking recommended in the ISO 14242 guidelines. These tests overlook the diverse motor tasks and lifestyle differences among patients. This study seeks to establish the foundation for In-Silico clinical trials of total hip replacements, enabling wear simulations of patients with different lifestyles, not feasible with In-Vitro tests. The impact of diverse kinematic and loading histories on the wear of metal-on-plastic hip replacements is investigated in a novel way, considering the combined effect of six daily activities (e.g., walking, fast walking, sit/stand, stairs up/down, lunging), different activity frequencies across five patient profiles (from sedentary elderly to active young), and the effect of load sequence. The results reveal that both the type and frequency of motor tasks significantly influence implant wear. The most critical tasks and at-risk patients were stair climbing and the most active individuals, regardless of age. Load sequence also plays a key role in long-term wear predictions. Accuracy and computational cost were balanced by simulating walking, stair climbing, and sit/stand cycles, ensuring equivalent wear to a complete motor task sequence. ISO standards conditions notably tend to underestimate volumetric wear by up to 60% compared to the simulated patient types. They also fail to predict realistic wear patterns for activities like squatting and lunging where edge contact occurs.

An open source statistical web application for validation and analysis of virtual cohorts

The conventional approach to developing medical treatments and medical devices usually covers pre-clinical and in-vitro investigations, in-vivo animal studies and clinical trials with humans. In-silico trials and virtual cohorts present a promising avenue for addressing the challenges inherent in clinical research and improving its efficiency. Despite considerable advancements in the field of in-silico trials, several notable gaps and challenges still need to be addressed, one is the limited availability of open and user-friendly statistical tools to support the specific analysis of virtual cohorts and in-silico trials. In the EU-Horizon funded project SIMCor we have developed a web application, providing a R-statistical environment supporting the validation of virtual cohorts and the application of validated cohorts for in-silico trials. It provides a practical platform for validating cohorts and has implemented existing statistical techniques that can be applied to compare virtual cohorts with real datasets. It is fully open, generic and menu driven and provides user guidance and help ( https://github.com/ecrin-github/SIMCor , https://zenodo.org/records/14718597 ).The tool has been developed according to specified user requirements and has been extensively tested and validated. Important next steps are to gain more experience with the tool in other domains and research environments and to extend its functionality.

Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer

Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we developed a quantitative cancer-immunity cycle (QCIC) model. The QCIC model employs differential equations to capture the biological mechanisms underlying the cancer-immunity cycle and predicts tumor evolution dynamics under various treatment strategies through stochastic computational methods. We introduce the treatment response index (TRI) to quantify disease progression in virtual clinical trials and the death probability function (DPF) to estimate overall survival. Additionally, we investigate the impact of predictive biomarkers on survival prognosis in advanced mCRC patients, identifying tumor-infiltrating CD8+ cytotoxic T lymphocytes (CTLs) as key predictors of disease progression and the tumor-infiltrating CD4+ Th1/Treg ratio as a significant determinant of survival outcomes. This study presents an approach that bridges the gap between diverse clinical data sources and the generation of virtual patient cohorts, providing valuable insights into interindividual treatment variability and survival forecasting in mCRC patients.

Advancing in Silico Clinical Trials for Regulatory Adoption and Innovation

The evolution of information and communication technologies has affected all fields of science, including health sciences. However, the rate of technological innovation adoption by the healthcare sector has been historically slow, compared to other industrial sectors. Innovation in computer modeling and simulation approaches has changed the landscape in biomedical applications and biomedicine, paving the way for their potential contribution in reducing, refining, and partially replacing animal and human clinical trials. In Silico Clinical Trials (ISCT) allow the development of virtual populations used in the safety and efficacy testing of new drugs and medical devices. This White Paper presents the current framework for ISCT, the role of in silico medicine research communities, the different perspectives (research, scientific, clinical, regulatory, standardization, data quality, legal and ethical), the barriers, challenges, and opportunities for ISCT adoption. In addition, an overview of successful ISCT projects, market-available platforms, and FDA- approved paradigms, along with their vision, mission and outcomes are presented.

SARS-CoV-2 proteins show great binding affinity to resin composite monomers and polymerized chains

BACKGROUND

Due to saliva and salivary glands are reservoir to severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), aerosols and saliva droplets are primary sources of cross-infection and are responsible for the high human-human transmission of SARS-CoV-2. However, there is no evidence about how SARS-CoV-2 interacts with oral structures, particularly resin composites.

AIM

To evaluate the interaction of SARS-CoV-2 proteins with monomers present in resin composites using in silico analysis.

METHODS

Four SARS-CoV-2 proteins [i.e. main protease, 3C-like protease, papain-like protease (PLpro), and glycoprotein spike] were selected along with salivary amylase as the positive control, and their binding affinity with bisphenol-A glycol dimethacrylate, bisphenol-A ethoxylated dimethacrylate, triethylene glycol dimethacrylate, and urethane dimethacrylate was evaluated. Molecular docking was performed using AutoDock Vina and visualised in Chimera UCSF 1.14. The best ligand-protein model was identified based on the binding energy (ΔG-kcal/moL).

RESULTS

Values for the binding energies ranged from -3.6 kcal/moL to -7.3 kcal/moL. The 3-monomer chain had the lowest binding energy (i.e. highest affinity) to PLpro and the glycoprotein spike. Non-polymerised monomers and polymerised chains interacted with SARS-CoV-2 proteins via hydrogen bonds and hydrophobic interactions. Those findings suggest an interaction between SARS-CoV-2 proteins and resin composites.

CONCLUSION

SARS-CoV-2 proteins show affinity to non-polymerised and polymerised resin composite chains.

Credibility assessment of a mechanistic model of atherosclerosis to predict cardiovascular outcomes under lipid-lowering therapy

Demonstrating cardiovascular (CV) benefits with lipid-lowering therapy (LLT) requires long-term randomized clinical trials (RCTs) with thousands of patients. Innovative approaches such as in silico trials applying a disease computational model to virtual patients receiving multiple treatments offer a complementary approach to rapidly generate comparative effectiveness data. A mechanistic computational model of atherosclerotic cardiovascular disease (ASCVD) was built from knowledge, describing lipoprotein homeostasis, LLT effects, and the progression of atherosclerotic plaques leading to myocardial infarction, ischemic stroke, major acute limb event and CV death. The ASCVD model was successfully calibrated and validated, and reproduced LLT effects observed in selected RCTs (ORION-10 and FOURIER for calibration; ORION-11, ODYSSEY-OUTCOMES and FOURIER-OLE for validation) on lipoproteins and ASCVD event incidence at both population and subgroup levels. This enables the future use of the model to conduct the SIRIUS programme, which intends to predict CV event reduction with inclisiran, an siRNA targeting hepatic PCSK9 mRNA.

In Silico Clinical Trial for Osteoporosis Treatments to Prevent Hip Fractures: Simulation of the Placebo Arm

Osteoporosis represents a major healthcare concern. The development of novel treatments presents challenges due to the limited cost-effectiveness of clinical trials and ethical concerns associated with placebo-controlled trials. Computational models for the design and assessment of biomedical products (In Silico Trials) are emerging as a promising alternative. In this study, a novel In Silico Trial technology (BoneStrength) was applied to replicate the placebo arms of two concluded clinical trials and its accuracy in predicting hip fracture incidence was evaluated. Two virtual cohorts (N = 1238 and 1226, respectively) were generated by sampling a statistical anatomy atlas based on CT scans of proximal femurs. Baseline characteristics were equivalent to those reported for the clinical cohorts. Fall events were sampled from a Poisson distribution. A multiscale stochastic model was implemented to estimate the impact force associated to each fall. Finite Element models were used to predict femur strength. Fracture incidence in 3 years follow-up was computed with a Markov chain approach; a patient was considered fractured if the impact force associated with a fall exceeded femur strength. Ten realizations of the stochastic process were run to reach convergence. Each realization required approximately 2500 FE simulations, solved using High-Performance Computing infrastructures. Predicted number of fractures was 12 ± 2 and 18 ± 4 for the two cohorts, respectively. The predicted incidence range consistently included the reported clinical data, although on average fracture incidence was overestimated. These findings highlight the potential of BoneStrength for future applications in drug development and assessment.

The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing

Metronomic chemotherapy (MC), long-term continuous administration of anticancer drugs, is gaining attention as an alternative to the traditional maximum tolerated dose (MTD) chemotherapy. By combining MC with other treatments, the therapeutic efficacy is enhanced while minimizing toxicity. MC employs multiple mechanisms, making it a versatile approach against various cancers. However, drug resistance limits the long-term effectiveness of MC, necessitating ongoing development of anticancer drugs. Traditional drug discovery is lengthy and costly due to processes like target protein identification, virtual screening, lead optimization, and safety and efficacy evaluations. Drug repurposing (DR), which screens FDA-approved drugs for new uses, is emerging as a cost-effective alternative. Both experimental and computational methods, such as protein binding assays, in vitro cytotoxicity tests, structure-based screening, and several types of association analyses (Similarity-Based, Network-Based, and Target Gene), along with retrospective clinical analyses, are employed for virtual screening. This review covers the mechanisms of MC, its application in various cancers, DR strategies, examples of repurposed drugs, and the associated challenges and future directions.

Identification of mitoxantrone as a potent inhibitor of CDK7/Cyclin H via structure-based virtual screening and In-Vitro validation by ADP-Glo kinase assay

Cyclin-dependent kinases, CDK7 and CDK9 play critical roles in cancer by regulating transcriptional processes essential for cell proliferation and survival. Their dysregulation leads to aberrant gene expression, promoting oncogenic pathways and contributing to tumor growth and progression. This study aimed to identify a new chemotype for CDK7/9 inhibitors using a structure-based virtual screening approach. Our research led to the discovery of mitoxantrone as an inhibitor of CDK7/H and CDK9/T1 from a library of FDA-approved small molecule drugs. Mitoxantrone, a chemotherapy agent used to treat acute nonlymphocytic leukemia, works by disrupting DNA synthesis and repair, thus inhibiting cancer cell growth. The study found that mitoxantrone effectively inhibits both CDK7/H and CDK9/T1 with IC50 values of 0.675 µM and 5.15 µM, respectively, while showing no inhibition of CDK2/E1 (IC50 > 100 µM) in in-vitro ADP-Glo kinase assay. It binds to the ATP pocket of CDK7 and CDK9, forming crucial H-bonds with MET 94 and CYS 106, respectively. It achieves dock scores of - 12.93 and - 12.59 kcal/mol, and MMGBSA binding energies of - 82.87 and - 81.59 kcal/mol, respectively. Molecular dynamics simulations over 100 ns confirmed stable interactions with MET 94 and CYS 106 in the hinge region of CDK7 and CDK9. The active site sequence alignment helped to understand the differential activity of mitoxantrone for CDK7, 9 and 2 inhibitions. The findings of the paper reveal a novel mechanism of mitoxantrone action that may contribute to its anticancer efficacy.

Off-label in-silico flow diverter performance assessment in posterior communicating artery aneurysms

BACKGROUND

The posterior communicating artery (PComA) is among the most common intracranial aneurysm locations, but flow diverter (FD) treatment with the widely used pipeline embolization device (PED) remains an off-label treatment that is not well understood. PComA aneurysm flow diversion is complicated by the presence of fetal posterior circulation (FPC), which has an estimated prevalence of 4-29% and is more common in people of black (11.5%) than white (4.9%) race. We present the FD-PComA in-silico trial (IST) into FD treatment performance in PComA aneurysms. ISTs use computational modeling and simulation in cohorts of virtual patients to evaluate medical device performance.

METHODS

We modeled FD treatment in 118 virtual patients with 59 distinct PComA aneurysm anatomies, using computational fluid dynamics to assess post-treatment outcome. Boundary conditions were prescribed to model the effects of non-fetal and FPC, allowing for comparison between these subgroups.

RESULTS

FD-PComA predicted reduced treatment success in FPC patients, with an average aneurysm space and time-averaged velocity reduction of 67.8% for non-fetal patients and 46.5% for fetal patients (P<0.001). Space and time-averaged wall shear stress on the device surface was 29.2 Pa averaged across fetal patients and 23.5 Pa across non-fetal (P<0.05) patients, suggesting FD endothelialization may be hindered in FPC patients. Morphological variables, such as the size and shape of the aneurysm and PComA size, did not affect the treatment outcome.

CONCLUSIONS

FD-PComA had significantly lower treatment success rates in PComA aneurysm patients with FPC. We suggest that FPC patients should be treated with an alternative to single PED flow diversion.

FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine

Importance

Advances in artificial intelligence (AI) must be matched by efforts to better understand and evaluate how AI performs across health care and biomedicine as well as develop appropriate regulatory frameworks. This Special Communication reviews the history of the US Food and Drug Administration's (FDA) regulation of AI; presents potential uses of AI in medical product development, clinical research, and clinical care; and presents concepts that merit consideration as the regulatory system adapts to AI's unique challenges.

Observations

The FDA has authorized almost 1000 AI-enabled medical devices and has received hundreds of regulatory submissions for drugs that used AI in their discovery and development. Health AI regulation needs to be coordinated across all regulated industries, the US government, and with international organizations. Regulators will need to advance flexible mechanisms to keep up with the pace of change in AI across biomedicine and health care. Sponsors need to be transparent about and regulators need proficiency in evaluating the use of AI in premarket development. A life cycle management approach incorporating recurrent local postmarket performance monitoring should be central to health AI development. Special mechanisms to evaluate large language models and their uses are needed. Approaches are necessary to balance the needs of the entire spectrum of health ecosystem interests, from large firms to start-ups. The evaluation and regulatory system will need to focus on patient health outcomes to balance the use of AI for financial optimization for developers, payers, and health systems.

Conclusions and Relevance

Strong oversight by the FDA protects the long-term success of industries by focusing on evaluation to advance regulated technologies that improve health. The FDA will continue to play a central role in ensuring safe, effective, and trustworthy AI tools to improve the lives of patients and clinicians alike. However, all involved entities will need to attend to AI with the rigor this transformative technology merits.

In silico medicine and -omics strategies in nephrology: contributions and relevance to the diagnosis and prevention of chronic kidney disease

Chronic kidney disease (CKD) has been increasing over the last years, with a rate between 0.49% to 0.87% new cases per year. Currently, the number of affected people is around 850 million worldwide. CKD is a slowly progressive disease that leads to irreversible loss of kidney function, end-stage kidney disease, and premature death. Therefore, CKD is considered a global health problem, and this sets the alarm for necessary efficient prediction, management, and disease prevention. At present, modern computer analysis, such as in silico medicine (ISM), denotes an emergent data science that offers interesting promise in the nephrology field. ISM offers reliable computer predictions to suggest optimal treatments in a case-specific manner. In addition, ISM offers the potential to gain a better understanding of the kidney physiology and/or pathophysiology of many complex diseases, together with a multiscale disease modeling. Similarly, -omics platforms (including genomics, transcriptomics, metabolomics, and proteomics), can generate biological data to obtain information on gene expression and regulation, protein turnover, and biological pathway connections in renal diseases. In this sense, the novel patient-centered approach in CKD research is built upon the combination of ISM analysis of human data, the use of in vitro models, and in vivo validation. Thus, one of the main objectives of CKD research is to manage the disease by the identification of new disease drivers, which could be prevented and monitored. This review explores the wide-ranging application of computational medicine and the application of -omics strategies in evaluating and managing kidney diseases.

The utility and impact of digital endpoints for improving breast cancer outcomes

INTRODUCTION

In breast cancer clinical trials utilizing digital endpoints, wearable sensors record participants' health information during activities of daily living.  These sensors are worn on the wrist or finger, placed as a skin patch or headband, or embedded on clothing. Data collected from wearable sensors form the basis of a digital endpoint, useful for determining effects of novel treatments on health outcomes, particularly quality-of-life outcomes.

AREAS COVERED

References for this article were selected from a PubMed search spanning from 1 January 2017,to 1 July 2024, using the terms 'wearable sensors,' 'digital endpoints,' 'virtualtrials,' 'breast cancer.'  Additional articles from the authors' personal collection of papers and reviewers suggestions were also used.

EXPERT OPINION

Digital endpoints must be validated as proper surrogate measures for healthcare outcomes, prior to their use in breast cancer trials.  Wearable sensors may introduce biases, such as 'missing not-at-random bias,' and perhaps even exacerbate disparities in healthcare outcomes if patients not comfortable with their use are excluded from clinical trials, or if the accuracy of sensors varies between racial and ethnic groups. Therefore, before embarking on trials with digital endpoints, validation studies are required, and limitations and risks of such trials need to be addressed.

Recommendations on Robotic Hepato-Pancreato-Biliary Surgery. The Paris Jury-Based Consensus Conference

OBJECTIVE

To establish the first consensus guidelines on the safety and indications of robotics in Hepato-Pancreatic-Biliary (HPB) surgery. The secondary aim was to identify priorities for future research.

BACKGROUND

HPB robotic surgery is reaching the IDEAL 2b exploration phase for innovative technology. An objective assessment endorsed by the HPB community is timely and needed.

METHODS

The ROBOT4HPB conference developed consensus guidelines using the Zurich-Danish model. An impartial and multidisciplinary jury produced unbiased guidelines based on the work of 10 expert panels answering predefined key questions and considering the best-quality evidence retrieved after a systematic review. The recommendations conformed with the GRADE and SIGN50 methodologies.

RESULTS

Sixty-four experts from 20 countries considered 285 studies, and the conference included an audience of 220 attendees. The jury (n=10) produced recommendations or statements covering 5 sections of robotic HPB surgery: technology, training and expertise, outcome assessment, and liver and pancreatic procedures. The recommendations supported the feasibility of robotics for most HPB procedures and its potential value in extending minimally invasive indications, emphasizing, however, the importance of expertise to ensure safety. The concept of expertise was defined broadly, encompassing requirements for credentialing HPB robotics at a given center. The jury prioritized relevant questions for future trials and emphasized the need for prospective registries, including validated outcome metrics for the forthcoming assessment of HPB robotics.

CONCLUSIONS

The ROBOT4HPB consensus represents a collaborative and multidisciplinary initiative, defining state-of-the-art expertise in HPB robotics procedures. It produced the first guidelines to encourage their safe use and promotion.

Mathematical model of the inflammatory response to acute and prolonged lipopolysaccharide exposure in humans

One in five deaths worldwide is associated with sepsis, which is defined as organ dysfunction caused by a dysregulated host response to infection. An increased understanding of the pathophysiology of sepsis could provide improved approaches for early detection and treatment. Here we describe the development and validation of a mechanistic mathematical model of the inflammatory response, making use of a combination of in vitro and human in vivo data obtained from experiments where bacterial lipopolysaccharide (LPS) was used to induce an inflammatory response. The new model can simulate the responses to both acute and prolonged inflammatory stimuli in an experimental setting, as well as the response to infection in the clinical setting. This model serves as a foundation for a sepsis simulation model with a potentially wide range of applications in different disciplines involved with sepsis research.

Prospective in silico trials identify combined SK and K2P channel block as an effective strategy for atrial fibrillation cardioversion

Virtual evaluation of medical therapy through human-based modelling and simulation can accelerate and augment clinical investigations. Treatment of the most common cardiac arrhythmia, atrial fibrillation (AF), requires novel approaches. This study prospectively evaluates and mechanistically explains three novel pharmacological therapies for AF through in silico trials, including single and combined SK and K2P channel block. AF and pharmacological action were assessed in a large cohort of 1000 virtual patients, through 2962 multiscale simulations. Extensive calibration and validation with experimental and clinical data support their credibility. Sustained AF was observed in 654 virtual patients. In this cohort, cardioversion efficacy increased to 82% (535 of 654) through combined SK+K2P channel block, from 33% (213 of 654) and 43% (278 of 654) for single SK and K2P blocks, respectively. Drug-induced prolongation of tissue refractoriness, dependent on the virtual patient's ionic current profile, explained cardioversion efficacy (atrial refractory period increase: 133.0 ± 48.4 ms for combined vs. 45.2 ± 43.0 and 71.0 ± 55.3 ms for single SK and K2P block, respectively). Virtual patients cardioverted by SK channel block presented lower K2P densities, while lower SK densities favoured the success of K2P channel inhibition. Both ionic currents had a crucial role on atrial repolarization, and thus a synergism resulted from the multichannel block. All three strategies, including the multichannel block, preserved atrial electrophysiological function (i.e. conduction velocity and calcium transient dynamics) and thus its contractile properties (safety). In silico trials identify key factors determining treatment success and the combined SK+K2P channel block as a promising strategy for AF management. KEY POINTS: This is a large-scale in silico trial study involving 2962 multiscale simulations. A population of 1000 virtual patients underwent three treatments for atrial fibrillation. Single and combined SK+K2P channel block were assessed prospectively. The multi-ion channel inhibition resulted in 82% cardioversion efficacy. In silico trials have broad implications for precision medicine.

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. 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.

PBBM Considerations for Base Models, Model Validation, and Application Steps: Workshop Summary Report

The proceedings from the 30th August 2023 (Day 2) of the workshop "Physiologically Based Biopharmaceutics Models (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives" are provided herein. Day 2 covered PBBM case studies from six regulatory authorities which provided considerations for model verification, validation, and application based on the context of use (COU) of the model. PBBM case studies to define critical material attribute (CMA) specification settings, such as active pharmaceutical ingredient (API) particle size distributions (PSDs) were shared. PBBM case studies to define critical quality attributes (CQAs) such as the dissolution specification setting or to define the bioequivalence safe space were also discussed. Examples of PBBM using the credibility assessment framework, COU and model risk assessment, as well as scientific learnings from PBBM case studies are provided. Breakout session discussions highlighted current trends and barriers to application of PBBMs including: (a) PBBM credibility assessment framework and level of validation, (b) use of disposition parameters in PBBM and points to consider when iv data are not available, (c) conducting virtual bioequivalence trials and dealing with variability, (d) model acceptance criteria, and (e) application of PBBMs for establishing safe space and failure edges.

Monte Carlo methods for medical imaging research

In radiation-based medical imaging research, computational modeling methods are used to design and validate imaging systems and post-processing algorithms. Monte Carlo methods are widely used for the computational modeling as they can model the systems accurately and intuitively by sampling interactions between particles and imaging subject with known probability distributions. This article reviews the physics behind Monte Carlo methods, their applications in medical imaging, and available MC codes for medical imaging research. Additionally, potential research areas related to Monte Carlo for medical imaging are discussed.

A novel strain-based bone-fracture healing algorithm is able to predict a range of healing outcomes

Fracture healing is a complex process which sometimes results in non-unions, leading to prolonged disability and high morbidity. Traditional methods of optimising fracture treatments, such as in vitro benchtop testing and in vivo randomised controlled trials, face limitations, particularly in evaluating the entire healing process. This study introduces a novel, strain-based fracture-healing algorithm designed to predict a wide range of healing outcomes, including both successful unions and non-unions. The algorithm uses principal strains as mechanical stimuli to simulate fracture healing in response to local mechanical environments within the callus region. The model demonstrates good agreement with experimental data from ovine metatarsal osteotomies across six fracture cases with varying gap widths and inter-fragmentary strains, replicates physiological bony growth patterns, and is independent of the initial callus geometry. This computational approach provides a framework for developing new fracture-fixation devices, aid in pre-surgical planning, and optimise rehabilitation strategies.

Exploring the connection between EU-funded research and methodological approaches: insights from a retrospective analysis

BACKGROUND

Over the last two decades, substantial investments have been directed towards supporting fundamental and applied research in Alzheimer's disease (AD), breast cancer (BC), and prostate cancer (PC), which continue to pose significant health challenges. Recently, the Joint Research Centre (JRC) of the European Commission (EC) conducted a retrospective analysis to examine the major scientific advancements resulting from EU-funded research in these disease areas and their impact on society.

METHODS

Building upon this analysis, our subsequent investigation delves into the methodological approaches-both animal and non-animal models and methods-employed in AD, BC, and PC research funded under past EU framework programs (FP5, FP6, FP7, and H2020), and explored the notable research outputs associated with these approaches.

RESULTS

Our findings indicate a prevalent use of animal-based methodologies in AD research, particularly evident in projects funded under H2020. Notably, projects focused on drug development, testing, or repurposing heavily relied on animal models. Conversely, research aimed at clinical trial design, patient stratification, diagnosis and diagnostic tool development, lifestyle interventions, and prevention-outputs with potential societal impact-more frequently utilised non-animal methods. Advanced investigations leveraging imaging, computational tools, biomarker discovery and organ/tissue chip technologies predominantly favoured non-animal strategies.

CONCLUSIONS

These insights highlight a correlation between methodological choices and the translational potential of research outcomes, suggesting the need for a reconsideration of research strategy planning in future framework programs.

Computational modelling of cardiovascular pathophysiology to risk stratify commercial spaceflight

For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight.

An update on nonhuman primate usage for drug and vaccine evaluation against filoviruses

INTRODUCTION

Due to their faithful recapitulation of human disease, nonhuman primates (NHPs) are considered the gold standard for evaluating drugs against Ebolavirus and other filoviruses. The long-term goal is to reduce the reliance on NHPs with more ethical alternatives. In silico simulations and organoid models have the potential to revolutionize drug testing by providing accurate, human-based systems that mimic disease processes and drug responses without the ethical concerns associated with animal testing. However, as these emerging technologies are still in their developmental infancy, NHP models are presently needed for late-stage evaluation of filovirus vaccines and drugs, as they provide critical insights into the efficacy and safety of new medical countermeasures.

AREAS COVERED

In this review, the authors introduce available NHP models and examine the existing literature on drug discovery for all medically significant filoviruses in corresponding models.

EXPERT OPINION

A deliberate shift toward animal-free models is desired to align with the 3Rs of animal research. In the short term, the use of NHP models can be refined and reduced by enhancing replicability and publishing negative data. Replacement involves a gradual transition, beginning with the selection and optimization of better small animal models; advancing organoid systems, and using in silico models to accurately predict immunological outcomes.

Unlocking the full potential of mesenchymal stromal cell therapy for osteoarthritis through machine learning-based in silico trials

Despite the potential of mesenchymal stromal cells (MSCs) in osteoarthritis (OA) treatment, the challenge lies in addressing their therapeutic inconsistency. Clinical trials revealed significantly varied therapeutic outcomes among patients receiving the same allogenic MSCs but different treatment regimens. Therefore, optimizing personalized treatment strategies is crucial to fully unlock MSCs' potential and enhance therapeutic consistency. We employed the XGBoost algorithm to train a self-collected database comprising 37 published clinical reports to create a model capable of predicting the probability of effective pain relief and Western Ontario and McMaster Universities (WOMAC) index improvement in OA patients undergoing MSC therapy. Leveraging this model, extensive in silico simulations were conducted to identify optimal personalized treatment strategies and ideal patient profiles. Our in silico trials predicted that the individually optimized MSC treatment strategies would substantially increase patients' chances of recovery compared to the strategies used in reported clinical trials, thereby potentially benefiting 78.1%, 47.8%, 94.4% and 36.4% of the patients with ineffective short-term pain relief, short-term WOMAC index improvement, long-term pain relief and long-term WOMAC index improvement, respectively. We further recommended guidelines on MSC number, concentration, and the patients' appropriate physical (body mass index, age, etc.) and disease states (Kellgren-Lawrence grade, etc.) for OA treatment. Additionally, we revealed the superior efficacy of MSC in providing short-term pain relief compared to platelet-rich plasma therapy for most OA patients. This study represents the pioneering effort to enhance the efficacy and consistency of MSC therapy through machine learning applied to clinical data. The in silico trial approach holds immense potential for diverse clinical applications.

Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM.

A GENERATIVE APPROACH TO TESTING THE PERFORMANCE OF PHYSIOLOGICAL CONTROL ALGORITHMS

Background

Physiological closed-loop control algorithms play an important role in the development of autonomous medical care systems, a promising area of research that has the potential to deliver healthcare therapies meeting each patient's specific needs. Computational approaches can support the evaluation of physiological closed-loop control algorithms considering various sources of patient variability that they may be presented with.

Method of Approach

In this paper, we present a generative approach to testing the performance of physiological closed-loop control algorithms. This approach exploits a generative physiological model (which consists of stochastic and dynamic components that represent diverse physiological behaviors across a patient population) to generate a select group of virtual subjects. By testing a physiological closed-loop control algorithm against this select group, the approach estimates the distribution of relevant performance metrics in the represented population. We illustrate the promise of this approach by applying it to a practical case study on testing a closed-loop fluid resuscitation control algorithm designed for hemodynamic management.

Results

In this context, we show that the proposed approach can test the algorithm against virtual subjects equipped with a wide range of plausible physiological characteristics and behavior, and that the test results can be used to estimate the distribution of relevant performance metrics in the represented population.

Conclusions

In sum, the generative testing approach may offer a practical, efficient solution for conducting pre-clinical tests on physiological closed-loop control algorithms.

Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

GPT-4 as a biomedical simulator

BACKGROUND

Computational simulation of biological processes can be a valuable tool for accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks. This study provides proof-of-concept for the use of GPT-4 as a versatile simulator of biological systems.

METHODS

We introduce SimulateGPT, a proof-of-concept for knowledge-driven simulation across levels of biological organization through structured prompting of GPT-4. We benchmarked our approach against direct GPT-4 inference in blinded qualitative evaluations by domain experts in four scenarios and in two quantitative scenarios with experimental ground truth. The qualitative scenarios included mouse experiments with known outcomes and treatment decision support in sepsis. The quantitative scenarios included prediction of gene essentiality in cancer cells and progression-free survival in cancer patients.

RESULTS

In qualitative experiments, biomedical scientists rated SimulateGPT's predictions favorably over direct GPT-4 inference. In quantitative experiments, SimulateGPT substantially improved classification accuracy for predicting the essentiality of individual genes and increased correlation coefficients and precision in the regression task of predicting progression-free survival.

CONCLUSION

This proof-of-concept study suggests that LLMs may enable a new class of biomedical simulators. Such text-based simulations appear well suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulations, but for which extensive knowledge is available as written text. Finally, we propose several directions for further development of LLM-based biomedical simulators, including augmentation through web search retrieval, integrated mathematical modeling, and fine-tuning on experimental data.

What public health challenges and unmet medical needs would benefit from interdisciplinary collaboration in the EU? A survey and multi-stakeholder debate

In the past decade, significant European calls for research proposals have supported translational collaborative research on non-communicable and infectious diseases within the biomedical life sciences by bringing together interdisciplinary and multinational consortia. This research has advanced our understanding of disease pathophysiology, marking considerable scientific progress. Yet, it is crucial to retrospectively evaluate these efforts' societal impact. Research proposals should be thoughtfully designed to ensure that the research findings can be effectively translated into actionable policies. In addition, the choice of scientific methods plays a pivotal role in shaping the societal impact of research discoveries. Understanding the factors responsible for current unmet public health issues and medical needs is crucial for crafting innovative strategies for research policy interventions. A multistakeholder survey and a roundtable helped identify potential needs for consideration in the EU research and policy agenda. Based on survey findings, mental health disorders, metabolic syndrome, cancer, antimicrobial resistance, environmental pollution, and cardiovascular diseases were considered the public health challenges deserving prioritisation. In addition, early diagnosis, primary prevention, the impact of environmental pollution on disease onset and personalised medicine approaches were the most selected unmet medical needs. Survey findings enabled the formulation of some research-policies interventions (RPIs), which were further discussed during a multistakeholder online roundtable. The discussion underscored recent EU-level activities aligned with the survey-derived RPIs and facilitated an exchange of perspectives on public health and biomedical research topics ripe for interdisciplinary collaboration and warranting attention within the EU's research and policy agenda. Actionable recommendations aimed at facilitating the translation of knowledge into transformative, science-based policies are also provided.

Advancing vaccine development against Opisthorchis viverrini: A synergistic integration of omics technologies and advanced computational tools

The liver fluke O. viverrini (Opisthorchis viverrini), a neglected tropical disease (NTD), endemic to the Great Mekong Subregion (GMS), mainly afflicts the northeastern region of Thailand. It is a leading cause of cholangiocarcinoma (CCA) in humans. Presently, the treatment modalities for opisthorchiasis incorporate the use of the antihelminthic drug praziquantel, the rapid occurrence of reinfection, and the looming threat of drug resistance highlight the urgent need for vaccine development. Recent advances in "omics" technologies have proven to be a powerful tool for such studies. Utilizing candidate proteins identified through proteomics and refined via immunoproteomics, reverse vaccinology (RV) offers promising prospects for designing vaccines targeting essential antibody responses to eliminate parasite. Machine learning-based computational tools can predict epitopes of candidate protein/antigens exhibiting high binding affinities for B cells, MHC classes I and II, indicating strong potential for triggering both humoral and cell-mediated immune responses. Subsequently, these vaccine designs can undergo population-specific testing and docking/dynamics studies to assess efficacy and synergistic immunogenicity. Hence, refining proteomics data through immunoinformatics and employing computational tools to generate antigen-specific targets for trials offers a targeted and efficient approach to vaccine development that applies to all domains of parasite infections. In this review, we delve into the strategic antigen selection process using omics modalities for the O. viverrini parasite and propose an innovative framework for vaccine design. We harness omics technologies to revolutionize vaccine development, promising accelerated discoveries and streamlined preclinical and clinical evaluations.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Toward Personalized Salbutamol Therapy: Validating Virtual Patient-Derived Population Pharmacokinetic Model with Real-World Data

Interindividual variability, influenced by patient-specific factors including age, weight, gender, race, and genetics, among others, contributes to variations in therapeutic response. Population pharmacokinetic (popPK) modeling is an essential tool for pinpointing measurable factors affecting dose-concentration relationships and tailoring dosage regimens to individual patients. Herein, we developed a popPK model for salbutamol, a short-acting β2-agonist (SABA) used in asthma treatment, to identify key patient characteristics that influence treatment response. To do so, synthetic data from physiologically-based pharmacokinetic (PBPK) models was employed, followed by an external validation using real patient data derived from an equivalent study. Thirty-two virtual patients were included in this study. A two-compartment model, with first-order absorption (no delay), and linear elimination best fitted our data, according to diagnostic plots and selection criteria. External validation demonstrated a strong agreement between individual predicted and observed values. The incorporation of covariates into the basic structural model identified a significant impact of age on clearance (Cl) and intercompartmental clearance (Q); gender on Cl and the constant rate of absorption (ka); race on Cl; and weight on Cl in the volume of distribution of the peripheral compartment (V2). This study addresses critical challenges in popPK modeling, particularly data scarcity, incompleteness, and homogeneity, in traditional clinical trials, by leveraging synthetic data from PBPK modeling. Significant associations between individual characteristics and salbutamol's PK parameters, here uncovered, highlight the importance of personalized therapeutic regimens for optimal treatment outcomes.

Derivation and Validation of Thresholds Using Synthetic Data Methods for Single-Test Screening of Emergency Department Patients with Possible Acute Myocardial Infarction Using a Point-of-Care Troponin Assay

BACKGROUND

Single-sample (screening) rule-out of acute myocardial infarction (AMI) with troponin requires derivation of a single-test screening threshold. In data sets with small event numbers, the lowest one or two concentrations of myocardial infarction (MI) patients dictate the threshold. This is not optimal. We aimed to demonstrate a process incorporating both real and synthetic data for deriving such thresholds using a novel pre-production high-precision point-of-care assay.

METHODS

cTnI concentrations were measured from thawed plasma using the Troponin I Next (TnI-Nx) assay (i-STAT; Abbott) in adults on arrival to the emergency department with symptoms suggestive of AMI. The primary outcome was an AMI or cardiac death within 30 days. We used internal-external validation with synthetic data production based on clinical and demographic data, plus the measured TnI-Nx concentration, to derive and validate decision thresholds for TnI-Nx. The target low-risk threshold was a sensitivity of 99% and a high-risk threshold specificity of >95%.

RESULTS

In total, 1356 patients were included, of whom 191 (14.1%) had the primary outcome. A total of 500 synthetic data sets were constructed. The mean low-risk threshold was determined to be 5 ng/L. This categorized 38% (95% CI, 6%-68%) to low-risk with a sensitivity of 99.0% (95% CI, 98.6%-99.5%) and a negative predictive value of 99.4% (95% CI, 97.6%-99.8%). A similarly derived high-risk threshold of 25 ng/L had a specificity of 95.0% (95% CI, 94.8%-95.1%) and a positive predictive value of 74.8% (95% CI, 71.5%-78.0%).

CONCLUSIONS

With the TnI-Nx assay, we successfully demonstrated an approach using synthetic data generation to derive low-risk thresholds for safe and effective screening.

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.

RESULTS

We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population.

CONCLUSIONS

This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Unlocking the potential of synthetic patients for accelerating clinical trials: Results of the first GIMEMA experience on acute myeloid leukemia patients

Artificial Intelligence has the potential to reshape the landscape of clinical trials through innovative applications, with a notable advancement being the emergence of synthetic patient generation. This process involves simulating cohorts of virtual patients that can either replace or supplement real individuals within trial settings. By leveraging synthetic patients, it becomes possible to eliminate the need for obtaining patient consent and creating control groups that mimic patients in active treatment arms. This method not only streamlines trial processes, reducing time and costs but also fortifies the protection of sensitive participant data. Furthermore, integrating synthetic patients amplifies trial efficiency by expanding the sample size. These straightforward and cost-effective methods also enable the development of personalized subject-specific models, enabling predictions of patient responses to interventions. Synthetic data holds great promise for generating real-world evidence in clinical trials while upholding rigorous confidentiality standards throughout the process. Therefore, this study aims to demonstrate the applicability and performance of these methods in the context of onco-hematological research, breaking through the theoretical and practical barriers associated with the implementation of artificial intelligence in medical trials.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Regulatory Framework for Implantable Neurostimulation Devices: Comparison of Systems in the US and European Union

BACKGROUND

Implantable neurostimulation devices must be authorized before they are placed on the market. For this purpose, requirements, and processes for assessing their fulfillment, have been defined in different jurisdictions.

OBJECTIVE

In this study, we aimed to address differences between the US and European Union (EU) regulatory systems and their relationship to innovation.

MATERIALS AND METHODS

A literature review and analysis were conducted using legal texts and guidance documents.

RESULTS

The US system has one central body, the Food and Drug Administration, whereas the EU system has several bodies with different responsibilities. The devices themselves are divided into risk classes, which are based on the vulnerability of the human body. This risk class determines the intensity of the review by the market authorization body. In addition to the requirements for development, manufacture, and distribution, the device itself must meet technical and clinical requirements. Compliance with technical requirements is indicated by nonclinical laboratory studies. Proof of efficacy is provided by means of clinical investigations. Procedures are defined for reviewing these elements. Once the market authorization process has been completed, the devices can be placed on the market. In the postmarketing phase, the devices must continue to be monitored, and measures must be initiated, if necessary.

CONCLUSIONS

Both US and EU systems are intended to ensure that only safe and effective devices find their way to and remain on the market. The basic approaches of the two systems are comparable. In detail, however, there are differences in ways these goals are achieved.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

Mechanistic insight of α-mangostin encapsulation in 2-hydroxypropyl-β-cyclodextrin for solubility enhancement

α-Mangostin is the most abundant compound contained in the mangostin (Garcinia mangostana L.) plant which have been developed and proven to have many promising pharmacological effects. However, the low water solubility of α-mangostin causes limitations in its development in clinical purpose. To increase the solubility of a compound, a method currently being developed is to make drug inclusion complexes using cyclodextrins. This research aimed to use in silico techniques namely molecular docking study and molecular dynamics simulation to explore the molecular mechanism and stability of the encapsulation of α-mangostin using cyclodextrins. Two types of cyclodextrins were used including β-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin docked against α-mangostin. From the molecular docking results, it shows that the α-mangostin complex with 2-hydroxypropyl-β-cyclodextrin provides the lowest binding energy value of -7.99 Kcal/mol compared to β-cyclodextrin value of -6.14 Kcal/mol. The α-mangostin complex with 2-hydroxypropyl-β-cyclodextrin also showed good stability based on molecular dynamics simulation during 100 ns. From molecular motion, RDF, Rg, SASA, density, total energy analyzes, this complex shows increased solubility in water and provided good stability. This indicates that the encapsulation of α-mangostin with 2-hydroxypropyl-β-cyclodextrin can increase the solubility of the α-mangostin.Communicated by Ramaswamy H. Sarma.

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice

OBJECTIVE

To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology.

METHODS

This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value.

RESULTS

RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring.

CONCLUSION

The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology.

CRITICAL RELEVANCE STATEMENT

The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation.

KEYPOINTS

• Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

Rapid on-site evaluation (ROSE) of image-guided FNA specimens improves subsequent core biopsy adequacy in clinical trial patients: The impact of preanalytical factors and its correlation with survival

BACKGROUND

Sufficient tumor collection has become of utmost importance in therapeutic experimental protocols. Rapid on-site evaluation (ROSE) ensures adequate sampling for quantification of biomarkers, molecular analyses, and other ancillary studies. The objectives of this study were to evaluate the role of ROSE in trial-associated fine-needle aspiration (FNA) and to analyze predictors of adequacy and cumulative survival from in-house FNA cases used in clinical trials.

METHODS

Clinical trial FNA biopsies performed at a large academic institution were analyzed over 10 months using a comprehensive chart review of the electronic medical records. SPSS version 28 was used for statistical analysis.

RESULTS

Three hundred twenty-five FNAs were collected for 57 clinical trials. In total, 225 individual patients had an average of 1.4 FNA procedures each as a result of a multidepartmental collaborative effort. ROSE was performed for all patients, and adequacy was evaluated by cytotechnologists. Seventy-eight percent of samples were considered adequate, 14% were considered less than optimal, and 8% were considered inadequate, with the latter two categories designated together as less than adequate. The imaging modalities were mainly ultrasound-guided (n = 267; 82%) and computed tomography-guided (n = 58; 18%). There was a statistically significant association between adequate sampling and ultrasound-guided biopsies (83%) compared with computed tomography-guided biopsies (59%; p < .01). The effect of body mass index (BMI) on mortality was also a significant finding. The authors observed a survival benefit in patients who had elevated BMIs (range, 25.0-34.9 kg/m2 ) compared with those who were underweight (BMI, <18.5 kg/m2 ) or class III obese (BMI, >35.0 kg/m2 ; p < .01). Therefore, the best predictors of adequacy and mortality were imaging modality and BMI, respectively.

CONCLUSIONS

Ultrasound-guided modalities are recommended for obtaining adequate FNA sampling for clinical trials. In addition, patients with cancer who had slightly elevated BMIs (25.0-34.0 kg/m2 ) had increased overall survival in this cohort.

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. 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.