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.

Human Monoclonal Antibody Cocktail for the Treatment or Prophylaxis of Middle East Respiratory Syndrome Coronavirus.

BACKGROUND: REGN3048 and REGN3051 are human monoclonal antibodies (mAb) targeting the spike glycoprotein on the Middle East respiratory syndrome coronavirus (MERS-CoV), which binds to the receptor dipeptidyl peptidase-4 (DPP4) and is necessary for infection of susceptible cells. METHODS: Preclinical study: REGN3048, REGN3051 and isotype immunoglobulin G (IgG) were administered to humanized DPP4 (huDPP4) mice 1 day prior to and 1 day after infection with MERS-CoV (Jordan strain). Virus titers and lung pathology were assessed. Phase 1 study: healthy adults received the combined mAb (n = 36) or placebo (n = 12) and followed for 121 days. Six dose levels were studied. Strict safety criteria were met prior to dose escalation. RESULTS: Preclinical study: REGN3048 plus REGN3051, prophylactically or therapeutically, was substantially more effective for reducing viral titer, lung inflammation, and pathology in huDPP4 mice compared with control antibodies and to each antibody monotherapy. Phase 1 study: REGN3048 plus REGN3051 was well tolerated with no dose-limiting adverse events, deaths, serious adverse events, or infusion reactions. Each mAb displayed pharmacokinetics expected of human IgG1 antibodies; it was not immunogenic. CONCLUSIONS: REGN3048 and REGN3051 in combination were well tolerated. The clinical and preclinical data support further development for the treatment or prophylaxis of MERS-CoV infection.

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.