Assessing the Combined Public Health Impact of Pharmaceutical Interventions on Pandemic Transmission and Mortality: An Example in SARS CoV-2.

To assess the combined role of anti-viral monoclonal antibodies (mAbs) and vaccines in reducing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) transmission and mortality in the United States, an agent-based model was developed that accounted for social contacts, movement/travel, disease progression, and viral shedding. The model was calibrated to coronavirus disease 2019 (COVID-19) mortality between October 2020 and April 2021 (aggressive pandemic phase), and projected an extended outlook to estimate mortality during a less aggressive phase (April-August 2021). Simulated scenarios evaluated mAbs for averting infections and deaths in addition to vaccines and aggregated non-pharmaceutical interventions. Scenarios included mAbs as a treatment of COVID-19 and for passive immunity for postexposure prophylaxis (PEP) during a period when variants were susceptible to the mAbs. Rapid diagnostic testing paired with mAbs was evaluated as an early treatment-as-prevention strategy. Sensitivity analyses included increasing mAb supply and vaccine rollout. Allocation of mAbs for use only as PEP averted up to 14% more infections than vaccine alone, and targeting individuals ≥ 65 years averted up to 37% more deaths. Rapid testing for earlier diagnosis and mAb use amplified these benefits. Doubling the mAb supply further reduced infections and mortality. mAbs provided benefits even as proportion of the immunized population increased. Model projections estimated that ~ 42% of expected deaths between April and August 2021 could be averted. Assuming sensitivity to mAbs, their use as early treatment and PEP in addition to vaccines would substantially reduce SARS-CoV-2 transmission and mortality even as vaccination increases and mortality decreases. These results provide a template for informing public health policy for future pandemic preparedness.

Using in silico viral kinetic models to guide therapeutic strategies during a pandemic: An example in SARS-CoV-2.

AIMS: We propose the use of in silico mathematical models to provide insights that optimize therapeutic interventions designed to effectively treat respiratory infection during a pandemic. A modelling and simulation framework is provided using SARS-CoV-2 as an example, considering applications for both treatment and prophylaxis. METHODS: A target cell-limited model was used to quantify the viral infection dynamics of SARS-CoV-2 in a pooled population of 105 infected patients. Parameter estimates from the resulting model were used to simulate and compare the impact of various interventions against meaningful viral load endpoints. RESULTS: Robust parameter estimates were obtained for the basic reproduction number, viral release rate and infected-cell mortality from the infection model. These estimates were informed by the largest dataset currently available for SARS-CoV-2 viral time course. The utility of this model was demonstrated using simulations, which hypothetically introduced inhibitory or stimulatory drug mechanisms at various target sites within the viral life-cycle. We show that early intervention is crucial to achieving therapeutic benefit when monotherapy is administered. In contrast, combination regimens of two or three drugs may provide improved outcomes if treatment is initiated late. The latter is relevant to SARS-CoV-2, where the period between infection and symptom onset is relatively long. CONCLUSIONS: The use of in silico models can provide viral load predictions that can rationalize therapeutic strategies against an emerging viral pathogen.

Blueprint for pandemic response: Focus on translational medicine, clinical pharmacology and pharmacometrics.

Perhaps the most important lesson learned from the COVID-19 pandemic is that of preparedness. Enhanced surveillance systems for early threat detection will be crucial to maximizing response time for implementation of public health measures and mobilization of resources in containing an emerging pandemic. Recent outbreaks have been dominated by viral pathogens, with RNA respiratory viruses being the most likely to have pandemic potential. These should therefore be a preparedness priority. Tools in the areas of virology, drug discovery, clinical pharmacology, translational medicine and pharmacometrics should be considered key components in the rapid identification and development of existing and novel interventions for a pandemic response. Prioritization of therapeutics should be based on in vitro activity, likelihood of achieving effective drug concentrations at the site of action, and safety profile at the doses that will be required for clinical efficacy. Deployment strategies must be tailored to the epidemiology of the disease, and the adequacy of the response should be re-evaluated in view of evolving epidemiological factors. An interdisciplinary framework integrating drug pharmacology, viral kinetics, epidemiology and health economics could help optimize the deployment strategy by improving decision-making around who to treat, when to treat, and with what type of intervention for optimal outcomes. Lastly, while an effective vaccine will ultimately end a pandemic, antiviral drug intervention guided by clinical pharmacology principles will continue to play a critical role in any pandemic response.

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.