To face SARS-CoV-2 pandemic various attempts are made to identify potential effective treatments by repurposing available drugs. Among them, indomethacin, an anti-inflammatory drug, was shown to have potent in-vitro antiviral properties on human SARS-CoV-1, canine CCoV, and more recently on human SARS-CoV-2 at low micromolar range. Our objective was to show that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 and to provide criteria for comparing benefits of alternative dosage regimens using a model-based approach. A multi-stage model-based approach was developed to characterize % of recovery and viral load in CCoV-infected dogs, to estimate the PK of indomethacin in dog and human using published data after administration of immediate (IR) and sustained-release (SR) formulations, and to estimate the expected antiviral activity as a function of different assumptions on the effective exposure in human. Different dosage regimens were evaluated for IR formulation (25 mg and 50 mg threetimes-a-day, and 25 mg four-times-a-day), and SR formulation (75 mg once and twice-a-day). The best performing dosing regimens were: 50 mg three-times-a-day for the IR formulation, and 75 mg twice-a-day for the SR formulation. The treatment with the SR formulation at the dose of 75 mg twice-a-day is expected to achieve a complete response in three days for the treatment in patients infected by the SARS-CoV-2 coronavirus. These results suggest that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 whose potential therapeutic effect need to be further assessed in a prospective clinical trial.

Model-based approach is recognized as a tool to make drug development more productive and to better support regulatory and therapeutic decisions. The objective of this study was to develop a novel model-based methodology based on the response surface analysis and a nonlinear optimizer algorithm to maximize the clinical performances of drug treatments. The treatment response was described using a drug-disease model accounting for multiple components such as the dosage regimen, the pharmacokinetic characteristics of a drug (including the mechanism and the rate of drug delivery), and the exposure-response relationship. Then, the clinical benefit of a treatment was defined as a function of the diseases and the clinical endpoints and was estimated as a function of the target pharmacodynamic endpoints used to evaluate the treatment effect. A case study is presented to illustrate how the treatment performances of paliperidone extended release (ER) and paliperidone long-acting injectable (LAI) can be improved. A convolution-based approach was used to characterize the pharmacokinetics of ER and LAI paliperidone. The drug delivery properties and the dosage regimen maximizing the clinical benefit (defined as the target level of D2 receptor occupancy) were estimated using a nonlinear optimizer. The results of the analysis indicated that a substantial improvement in clinical benefit (from 15% to 27% for the optimization of the in vivo release and from ∼30% to ∼70% for the optimization of dosage regimen) was obtained when optimal strategies were deployed either for optimizing the in vivo drug delivery properties of ER formulations or for optimizing the dosage regimen of LAI formulations.

The net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit‐risk ratio. The optimization of the benefit‐risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best‐performing in vivo release properties of a drug. A general in silico tool is presented for identifying the dose, the in vitro and the in vivo release properties that maximize the benefit‐risk ratio using convolution‐based modeling, an exposure‐response model, and a surface response analysis. A case study is presented to illustrate how the benefit‐risk ratio of methylphenidate for the treatment of attention deficit hyperactivity disorder can be maximized using the proposed strategy. The results of the analysis identified the characteristics of an optimized dose and in vitro / in vivo release suitable to provide a sustained clinical response with respect to the conventional dosage regimen and formulations.

To face SARS-CoV-2 pandemic various attempts are made to identify potential effective treatments by repurposing available drugs. Among them, indomethacin, an anti-inflammatory drug, was shown to have potent in-vitro antiviral properties on human SARS-CoV-1, canine CCoV, and more recently on human SARS-CoV-2 at low micromolar range. Our objective was to show that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 and to provide criteria for comparing benefits of alternative dosage regimens using a model-based approach.A multi-stage model-based approach was developed to characterize % of recovery and viral load in CCoV-infected dogs, to estimate the PK of indomethacin in dog and human using published data after administration of immediate (IR) and sustained-release (SR) formulations, and to estimate the expected antiviral activity as a function of different assumptions on the effective exposure in human. Different dosage regimens were evaluated for IR formulation (25 mg and 50 mg three-times-a-day, and 25 mg four-times-a-day), and SR formulation (75 mg once and twice-a-day). The best performing dosing regimens were: 50 mg three-times-a-day for the IR formulation, and 75 mg twice-a-day for the SR formulation. The treatment with the SR formulation at the dose of 75 mg twice-a-day is expected to achieve a complete response in three days for the treatment in patients infected by the SARS-CoV-2 coronavirus. These results suggest that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 whose potential therapeutic effect needs to be further assessed in a prospective clinical trial.

No abstract