Model-informed drug repurposing: Viral kinetic modelling to prioritize rational drug combinations for COVID-19

SAAM II: A general mathematical modeling rapid prototyping environment

Simulation Analysis and Modeling II (SAAM II) is a graphical modeling software used in life sciences for compartmental model analysis, particularly, but not exclusively, appreciated in pharmacokinetics (PK) and pharmacodynamics (PD), metabolism, and tracer modeling. Its intuitive "circles and arrows" visuals allow users to easily build, solve, and fit compartmental models without the need for coding. It is suitable for rapid prototyping of models for complex kinetic analysis or PK/PD problems, and in educating students and non-modelers. Although it is straightforward in design, SAAM II incorporates sophisticated algorithms programmed in C to address ordinary differential equations, deal with complex systems via forcing functions, conduct multivariable regression featuring the Bayesian maximum a posteriori, perform identifiability and sensitivity analyses, and offer reporting functionalities, all within a single package. After 26 years from the last SAAM II tutorial paper, we demonstrate here SAAM II's updated applicability to current life sciences challenges. We review its features and present four contemporary case studies, including examples in target-mediated PK/PD, CAR-T-cell therapy, viral dynamics, and transmission models in epidemiology. Through such examples, we demonstrate that SAAM II provides a suitable interface for rapid model selection and prototyping. By enabling the fast creation of detailed mathematical models, SAAM II addresses a unique requirement within the mathematical modeling community.

The British Journal of Clinical Pharmacology: The first 50 years

The British Journal of Clinical Pharmacology celebrates its 50th anniversary of publication in 2023. Here four previous Editors-in-Chief and the current Editor reflect on the Journal's history and the changes that have occurred during that time.

High sera levels of SARS-CoV-2 N antigen are associated with death in hospitalized COVID-19 patients

The presence of free severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid-antigen in sera (N-antigenemia) has been shown in COVID-19 patients. However, the link between the quantitative levels of N-antigenemia and COVID-19 disease severity is not entirely understood. To assess the dynamics and clinical association of N-antigen sera levels with disease severity in COVID-19 patients, we analyzed data from patients included in the French COVID cohort, with at least one sera sample between January and September 2020. We assessed N-antigenemia levels and anti-N IgG titers, and patient outcomes was classified in two groups, survival or death. In samples collected within 8 days since symptom onset, we observed that deceased patients had a higher positivity rate (93% vs. 81%; p < 0.001) and higher median levels of predicted N-antigenemia (2500 vs. 1200 pg/mL; p < 0.001) than surviving patients. Predicted time to N-antigen clearance in sera was prolonged in deceased patients compared to survivors (23.3 vs 19.3 days; p < 0.0001). In a subset of patients with both sera and nasopharyngeal (NP) swabs, predicted time to N-antigen clearance in sera was prolonged in deceased patients (p < 0.001), whereas NP viral load clearance did not differ between the groups (p = 0.07). Our results demonstrate a strong relationship between N-antigenemia levels and COVID-19 severity on a prospective cohort.

Drug Cocktail Formulation via Circuit Design

Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.

Comparative assessment of viral dynamic models for SARS-CoV-2 for pharmacodynamic assessment in early treatment trials

Pharmacometric analyses of time series viral load data may detect drug effects with greater power than approaches using single time points. Because SARS-CoV-2 viral load rapidly rises and then falls, viral dynamic models have been used. We compared different modelling approaches when analysing Phase II-type viral dynamic data. Using two SARS-CoV-2 datasets of viral load starting within 7 days of symptoms, we fitted the slope-intercept exponential decay (SI), reduced target cell limited (rTCL), target cell limited (TCL) and TCL with eclipse phase (TCLE) models using nlmixr. Model performance was assessed via Bayesian information criterion (BIC), visual predictive checks (VPCs), goodness-of-fit plots, and parameter precision. The most complex (TCLE) model had the highest BIC for both datasets. The estimated viral decline rate was similar for all models except the TCL model for dataset A with a higher rate (median [range] day^-1 : dataset A; 0.63 [0.56-1.84]; dataset B: 0.81 [0.74-0.85]). Our findings suggest simple models should be considered during pharmacodynamic model development.

Dose optimisation and scarce resource allocation: two sides of the same coin

OBJECTIVE

A deep understanding of the relationship between a scarce drug's dose and clinical response is necessary to appropriately distribute a supply-constrained drug along these lines.

SUMMARY OF KEY DATA

The vast majority of drug development and repurposing during the COVID-19 pandemic - an event that has made clear the ever-present scarcity in healthcare systems -has been ignorant of scarcity and dose optimisation's ability to help address it.

CONCLUSIONS

Future pandemic clinical trials systems should obtain dose optimisation data, as these appear necessary to enable appropriate scarce resource allocation according to societal values.

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

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.

Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

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

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

Treatment of Respiratory Viral Coinfections

With the advent of rapid multiplex PCR, physicians have been able to test for multiple viral pathogens when a patient presents with influenza-like illness. This has led to the discovery that many respiratory infections are caused by more than one virus. Antiviral treatment of viral coinfections can be complex because treatment of one virus will affect the time course of the other virus. Since effective antivirals are only available for some respiratory viruses, careful consideration needs to be given on the effect treating one virus will have on the dynamics of the other virus, which might not have available antiviral treatment. In this study, we use mathematical models of viral coinfections to assess the effect of antiviral treatment on coinfections. We examine the effect of the mechanism of action, relative growth rates of the viruses, and the assumptions underlying the interaction of the viruses. We find that high antiviral efficacy is needed to suppress both infections. If high doses of both antivirals are not achieved, then we run the risk of lengthening the duration of coinfection or even of allowing a suppressed virus to replicate to higher viral titers.

Model-informed drug repurposing: Viral kinetic modelling to prioritize rational drug combinations for COVID-19

AIM

We hypothesized that viral kinetic modelling could be helpful to prioritize rational drug combinations for COVID-19. The aim of this research was to use a viral cell cycle model of SARS-CoV-2 to explore the potential impact drugs, or combinations of drugs, that act at different stages in the viral life cycle might have on various metrics of infection outcome relevant in the early stages of COVID-19 disease.

METHODS

Using a target-cell limited model structure that has been used to characterize viral load dynamics from COVID-19 patients, we performed simulations to inform on the combinations of therapeutics targeting specific rate constants. The endpoints and metrics included viral load area under the curve (AUC), duration of viral shedding and epithelial cells infected. Based on the known kinetics of the SARS-CoV-2 life cycle, we rank ordered potential targeted approaches involving repurposed, low-potency agents.

RESULTS

Our simulations suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time-window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype.

CONCLUSIONS

Our work highlights the use of model-informed drug repurposing approaches to better rationalize effective treatments for COVID-19.

Medicinal Plants in COVID-19: Potential and Limitations

Currently, the search to identify treatments and vaccines for novel coronavirus disease (COVID-19) are ongoing. Desperation within the community, especially among the middle-and low-income groups acutely affected by the economic impact of forced lockdowns, has driven increased interest in exploring alternative choices of medicinal plant-based therapeutics. This is evident with the rise in unsubstantiated efficacy claims of these interventions circulating on social media. Based on enquiries received, our team of researchers was given the chance to produce evidence summaries evaluating the potential of complementary interventions in COVID-19 management. Here, we present and discuss the findings of four selected medicinal plants (Nigella sativa, Vernonia amygdalina, Azadirachta indica, Eurycoma longifolia), with reported antiviral, anti-inflammatory, and immunomodulatory effects that might be interesting for further investigation. Our findings showed that only A. indica reported positive antiviral evidence specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on preliminary in silico data while all four medicinal plants demonstrated differential anti-inflammatory or immunomodulatory effects. The definitive roles of these medicinal plants in cytokine storms and post-infection complications remains to be further investigated. Quality control and standardisation of medicinal plant-based products also needs to be emphasized. However, given the unprecedented challenges faced, ethnopharmacological research should be given a fair amount of consideration for contribution in this pandemic.

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Systems and Clinical Pharmacology of COVID-19 Therapeutic Candidates: A Clinical and Translational Medicine Perspective

Over 50 million people have been infected with the SARS-CoV-2 virus, while around 1 million have died due to COVID-19 disease progression. COVID-19 presents flu-like symptoms that can escalate, in about 7-10 days from onset, into a cytokine storm causing respiratory failure and death. Although social distancing reduces transmissibility, COVID-19 vaccines and therapeutics are essential to regain socioeconomic normalcy. Even if effective and safe vaccines are found, pharmacological interventions are still needed to limit disease severity and mortality. Integrating current knowledge and drug candidates (approved drugs for repositioning among >35 candidates) undergoing clinical studies (>3000 registered in ClinicalTrials.gov), we employed Systems Pharmacology approaches to project how antivirals and immunoregulatory agents could be optimally evaluated for use. Antivirals are likely to be effective only at the early stage of infection, soon after exposure and before hospitalization, while immunomodulatory agents should be effective in the later-stage cytokine storm. As current antiviral candidates are administered in hospitals over 5-7 days, a long-acting combination that targets multiple SARS-CoV-2 lifecycle steps may provide a long-lasting, single-dose treatment in outpatient settings. Long-acting therapeutics may still be needed even when vaccines become available as vaccines are likely to be approved based on a 50% efficacy target.

Model-informed drug repurposing: A pharmacometric approach to novel pathogen preparedness, response and retrospection

During a pandemic caused by a novel pathogen (NP), drug repurposing offers the potential of a rapid treatment response via a repurposed drug (RD) while more targeted treatments are developed. Five steps of model‐informed drug repurposing (MIDR) are discussed: (i) utilize RD product label and in vitro NP data to determine initial proof of potential, (ii) optimize potential posology using clinical pharmacokinetics (PK) considering both efficacy and safety, (iii) link events in the viral life cycle to RD PK, (iv) link RD PK to clinical and virologic outcomes, and optimize clinical trial design, and (v) assess RD treatment effects from trials using model‐based meta‐analysis. Activities which fall under these five steps are categorized into three stages: what can be accomplished prior to an NP emergence (preparatory stage), during the NP pandemic (responsive stage) and once the crisis has subsided (retrospective stage). MIDR allows for extraction of a greater amount of information from emerging data and integration of disparate data into actionable insight.

Early Treatment of COVID-19 Disease: A Missed Opportunity

Antivirals have demonstrated efficacy in treating other infectious diseases in early stages of disease, reducing morbidity, mortality, and the likelihood of onward transmission. At the time of writing, more than 1900 clinical trials are registered globally to assess the efficacy and safety of candidate therapeutics for COVID-19. The majority of these trials are designed to evaluate the comparative efficacy and safety of candidate therapeutics for the treatment of COVID-19 to prevent death among populations of hospitalized patients with advanced disease. Yet, emerging epidemiological evidence now indicates that the majority of those infected with the SARS-CoV-2, while still infectious, experience minimal or mild disease symptomology. Like HIV and hepatitis C that pioneered treatment as prevention, there is a missed opportunity for trials of early pharmaceutical intervention for COVID-19 disease evaluating not only reductions in morbidity and mortality but also transmissibility. We discuss this clinical research gap within an historical context of viral treatment as prevention for HIV and hepatitis C, and comment on the challenges and opportunities for clinical research of candidate therapeutics for early COVID-19 disease.

Accelerating Clinical Evaluation of Repurposed Combination Therapies for COVID-19

As the global COVID-19 pandemic continues, unabated and clinical trials demonstrate limited effective pharmaceutical interventions, there is a pressing need to accelerate treatment evaluations. Among options for accelerated development is the evaluation of drug combinations in the absence of prior monotherapy data. This approach is appealing for a number of reasons. First, combining two or more drugs with related or complementary therapeutic effects permits a multipronged approach addressing the variable pathways of the disease. Second, if an individual component of a combination offers a therapeutic effect, then in the absence of antagonism, a trial of combination therapy should still detect individual efficacy. Third, this strategy is time saving. Rather than taking a stepwise approach to evaluating monotherapies, this strategy begins with testing all relevant therapeutic options. Finally, given the severity of the current pandemic and the absence of treatment options, the likelihood of detecting a treatment effect with combination therapy maintains scientific enthusiasm for evaluating repurposed treatments. Antiviral combination selection can be facilitated by insights regarding SARS-CoV-2 pathophysiology and cell cycle dynamics, supported by infectious disease and clinical pharmacology expert advice. We describe a clinical evaluation strategy using adaptive combination platform trials to rapidly test combination therapies to treat COVID-19.