We thank Andrew W

We thank Andrew W. and predict efficacy of MK-1654 in the infant target population. Findings The MBMA established a quantitative relationship between RSV SNA and clinical endpoints. This relationship was quantitatively consistent with animal model challenge experiments and results of a recently published clinical trial. MRT68921 dihydrochloride Additionally, SNA elicited by increasing doses of MK-1654 in humans reduced RSV symptomatic contamination rates with a quantitative relationship that approximated the MBMA. The MBMA indicated a high probability that a single dose of ?75?mg of MK-1654 will result in prophylactic efficacy (>?75% for 5 months) in infants. Interpretation An MBMA approach can predict efficacy of neutralizing antibodies against RSV and MRT68921 dihydrochloride potentially other respiratory pathogens. Keywords: Respiratory Syncytial Computer virus, Monoclonal Antibody, RSV, Meta-analysis, Modelling and Simulation, Human Challenge Study Research in context Evidence before this study Respiratory syncytial computer virus (RSV) is usually a common pathogen that causes acute respiratory contamination, especially in infants, wherein it is the leading cause of hospitalization. The computer virus most commonly circulates seasonally, primarily in winter. Novel RSV neutralizing monoclonal antibodies (mAbs) with a long duration of activity (i.e., months), such as MK-1654, are a promising prophylactic approach for the prevention of disease in infants. With a single dose, these antibodies have the potential to prevent disease for an entire winter. Historically, selecting a dose for RSV mAb clinical candidates has relied on animal studies to approximate effective drug levels in humans. This approach does not take into account important factors, such as the duration of protection over time and the amount of drug needed in different patient populations. Thus, more predictive quantitative techniques based on human data are needed to guideline clinical dose prediction for antibodies that prevent RSV, as well as other respiratory viruses. Added value of this study Here, we report work that uses a mathematical model based on mechanistic understanding to integrate data from previously published RSV studies. This model accounts for the effects of drug, time, and patient population on clinical outcomes. By incorporating decades of qualified published clinical RSV prevention data, the mathematical model enables a quantitative understanding of the associations between antibody concentrations (titres) and protection from RSV disease for mAb prophylaxis, as well as for vaccines. Further, by validating our model predictions using animal studies, a published infant trial, and a PTGS2 controlled RSV contamination (challenge) clinical trial of MK-1654 in adults (described here for the first time), we advance the field’s ability to accurately predict the prophylactic efficacy of RSV mAbs and vaccines alike. Finally, the model was used to predict the efficacy of MK-1654 across a range of potential infant doses, providing confidence in the degree of protection from RSV contamination this antibody can afford. Implications of all the available evidence The work described here lays the foundation for an approach that will aid the design and interpretation of clinical trials for RSV and other pathogens. This method enables the prediction of doses and frequencies of administration needed to achieve protection for monoclonal antibodies and can similarly inform the development of vaccines. Alt-text: Unlabelled box 1.?Introduction Globally, human health is threatened with deadly viral pathogens ranging from localized outbreaks, yearly epidemics, to worldwide pandemics. Neutralizing antibodies, whether elicited by vaccines or introduced by the administration of mAbs, can prevent disease for many respiratory pathogens [1], [2], [3]. However, the dose selection process to achieve efficacious titres for vaccine and mAb clinical candidates has historically been performed without the benefit of MRT68921 dihydrochloride support from quantitative models. Doses are frequently derived either empirically or directly from animal models that may not accurately translate to humans [4]. The use of well-validated model-informed approaches for the prediction of clinically efficacious doses can facilitate efficient development of.