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.
The role of the host immune response in determining the severity and duration of an influenza infection is still unclear. In order to identify severity factors and more accurately predict the course ...of an influenza infection within a human host, an understanding of the impact of host factors on the infection process is required. Despite the lack of sufficiently diverse experimental data describing the time course of the various immune response components, published mathematical models were constructed from limited human or animal data using various strategies and simplifying assumptions. To assess the validity of these models, we assemble previously published experimental data of the dynamics and role of cytotoxic T lymphocytes, antibodies, and interferon and determined qualitative key features of their effect that should be captured by mathematical models. We test these existing models by confronting them with experimental data and find that no single model agrees completely with the variety of influenza viral kinetics responses observed experimentally when various immune response components are suppressed. Our analysis highlights the strong and weak points of each mathematical model and highlights areas where additional experimental data could elucidate specific mechanisms, constrain model design, and complete our understanding of the immune response to influenza.
Increasing multidrug resistance in Gram-negative bacteria, in particular
Pseudomonas aeruginosa, Acinetobacter baumannii, and
Klebsiella pneumoniae, presents a critical problem. Limited therapeutic ...options have forced infectious disease clinicians and microbiologists to reappraise the clinical application of colistin, a polymyxin antibiotic discovered more than 50 years ago. We summarise recent progress in understanding the complex chemistry, pharmacokinetics, and pharmacodynamics of colistin, the interplay between these three aspects, and their effect on the clinical use of this important antibiotic. Recent clinical findings are reviewed, focusing on evaluation of efficacy, emerging resistance, potential toxicities, and combination therapy. In the battle against rapidly emerging bacterial resistance we can no longer rely entirely on the discovery of new antibiotics; we must also pursue rational approaches to the use of older antibiotics such as colistin.
COVID-19 has had negative repercussions on the entire global population. Despite there being a common goal that should have unified resources and efforts, there have been an overwhelmingly large ...number of clinical trials that have been registered that are of questionable methodological quality. As the final paper of this Series, we discuss how the medical research community has responded to COVID-19. We recognise the incredible pressure that this pandemic has put on researchers, regulators, and policy makers, all of whom were doing their best to move quickly but safely in a time of tremendous uncertainty. However, the research community's response to the COVID-19 pandemic has prominently highlighted many fundamental issues that exist in clinical trial research under the current system and its incentive structures. The COVID-19 pandemic has not only re-emphasised the importance of well designed randomised clinical trials but also highlighted the need for large-scale clinical trials structured according to a master protocol in a coordinated and collaborative manner. There is also a need for structures and incentives to enable faster data sharing of anonymised datasets, and a need to provide similar opportunities to those in high-income countries for clinical trial research in low-resource regions where clinical trial research receives considerably less research funding.
Thrombocytopenia is a common side effect of linezolid, an oxazolidinone antibiotic often used to treat multidrug-resistant Gram-positive bacterial infections. Various risk factors have been ...suggested, including linezolid dose and duration of therapy, baseline platelet counts, and renal dysfunction; still, the mechanisms behind this potentially treatment-limiting toxicity are largely unknown. A clinical study was conducted to investigate the relationship between linezolid pharmacokinetics and toxicodynamics and inform strategies to prevent and manage linezolid-associated toxicity. Forty-one patients received 42 separate treatment courses of linezolid (600 mg every 12 h). A new mechanism-based, population pharmacokinetic/toxicodynamic model was developed to describe the time course of plasma linezolid concentrations and platelets. A linezolid concentration of 8.06 mg/liter (101% between-patient variability) inhibited the synthesis of platelet precursor cells by 50%. Simulations predicted treatment durations of 5 and 7 days to carry a substantially lower risk than 10- to 28-day therapy for platelet nadirs of <100 ×10(9)/liter. The risk for toxicity did not differ noticeably between 14 and 28 days of therapy and was significantly higher for patients with lower baseline platelet counts. Due to the increased risk of toxicity after longer durations of linezolid therapy and large between-patient variability, close monitoring of patients for development of toxicity is important. Dose individualization based on plasma linezolid concentration profiles and platelet counts should be considered to minimize linezolid-associated thrombocytopenia. Overall, oxazolidinone therapy over 5 to 7 days even at relatively high doses was predicted to be as safe as 10-day therapy of 600 mg linezolid every 12 h.
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.
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