Semi-Mechanistic Pharmacokinetic-Pharmacodynamic Model of Camostat Mesylate-Predicted Efficacy against SARS-CoV-2 in COVID-19.

The SARS-CoV-2 coronavirus, which causes COVID-19, uses a viral surface spike protein for host cell entry and the human cell-surface transmembrane serine protease, TMPRSS2, to process the spike protein. Camostat mesylate, an orally available and clinically used serine protease inhibitor, inhibits TMPRSS2, supporting clinical trials to investigate its use in COVID-19. A one-compartment pharmacokinetic (PK)/pharmacodynamic (PD) model for camostat and the active metabolite FOY-251 was developed, incorporating TMPRSS2 reversible covalent inhibition by FOY-251, and empirical equations linking TMPRSS2 inhibition of SARS-CoV-2 cell entry. The model predicts that 95% inhibition of TMPRSS2 is required for 50% inhibition of viral entry efficiency. For camostat 200 mg dosed four times daily, 90% inhibition of TMPRSS2 is predicted to occur but with only about 40% viral entry inhibition. For 3-fold higher camostat dosing, marginal improvement of viral entry rate inhibition, up to 54%, is predicted. Because respiratory tract viral load may be associated with negative outcome, even modestly reducing viral entry and respiratory tract viral load may reduce disease progression. This modeling also supports medicinal chemistry approaches to enhancing PK/PD and potency of the camostat molecule. IMPORTANCE Strategies to repurpose already-approved drugs for the treatment of COVID-19 has been attractive since the beginning of the pandemic. Camostat mesylate, a serine protease inhibitor approved in Japan for the treatment of acute exacerbations of chronic pancreatitis, inhibits TMPRSS1, a host cell surface serine protease essential for SARS-CoV-2 viral entry. In vitro experiments provided data suggesting that camostat might be effective in the treatment of COVID-19. Multiple clinical trials were planned to test the hypothesis that camostat would be beneficial for treating COVID-19 (for example, clinicaltrials.gov, NCT04353284). The present work used a one-compartment pharmacokinetic (PK)/pharmacodynamic (PD) mathematical model for camostat and the active metabolite FOY-251, incorporating TMPRSS2 reversible covalent inhibition by FOY-251, and empirical equations linking TMPRSS2 inhibition of SARS-CoV-2 cell entry. This work is valuable to guide further development of camostat mesylate and possible medicinal chemistry derivatives for the treatment of COVID-19.

Individualized treatment strategies for hyperuricemia informed by a semi-mechanistic exposure-response model of uric acid dynamics.

To provide insight into pharmacological treatment of hyperuricemia we developed a semi-mechanistic, dynamical model of uric acid (UA) disposition in human. Our model represents the hyperuricemic state in terms of production of UA (rate, PUA), its renal filtration (glomerular filtration rate, GFR) and proximal tubular reabsorption (fractional excretion coefficient, FE). Model parameters were estimated using data from 9 Phase I studies of xanthine oxidase inhibitors (XOI) allopurinol and febuxostat and a novel uricosuric, the selective UA reabsorption inhibitor lesinurad, approved for use in combination with a XOI. The model was qualified for prediction of the effect of patients' GFR and FE on concentration of UA in serum (sUA) and UA excretion in urine and their response to drug treatment, using data from 2 Phase I and 4 Phase III studies of lesinurad. Percent reduction in sUA from baseline by a XOI is predicted to be independent of GFR, FE or PUA. Uricosurics are more effective in underexcreters of UA or patients with normal GFR. Co-administration of a XOI and an uricosuric agent should be considered for patients with high sUA first in the treatment algorithm of gout before uptitration of XOI. The XOI dose in combination with a uricosuric can be reduced compared to XOI alone for the same target sUA to the degree dependent on patient's GFR and FE. This exposure-response model of UA can be used to rationally select the best drug treatment option to lower elevated sUA in gout patients under differing pathophysiological situations.

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

BACKGROUND: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS: The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS: This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

Characterization and Prediction of Cardiovascular Effects of Fingolimod and Siponimod Using a Systems Pharmacology Modeling Approach.

Sphingosine 1-phosphate (S1P) receptor agonists are associated with cardiovascular effects in humans. This study aims to develop a systems pharmacology model to identify the site of action (i.e., primary hemodynamic response variable) of S1P receptor agonists, and to predict, in a quantitative manner, the cardiovascular effects of novel S1P receptor agonists in vivo. The cardiovascular effects of once-daily fingolimod (0, 0.1, 0.3, 1, 3, and 10 mg/kg) and siponimod (3 and 15 mg/kg) were continuously recorded in spontaneously hypertensive rats and Wistar-Kyoto rats. The results were analyzed using a recently developed systems cardiovascular pharmacology model, i.e. the CVS model; total peripheral resistance and heart rate were identified as the site of action for fingolimod. Next, the CVS model was interfaced with an S1P agonist pharmacokinetic-pharmacodynamic (PKPD) model. This combined model adequately predicted, in a quantitative manner, the cardiovascular effects of siponimod using in vitro binding assays. In conclusion, the combined CVS and S1P agonist PKPD model adequately describes the hemodynamic effects of S1P receptor agonists in rats and constitutes a basis for the prediction, in a strictly quantitative manner, of the cardiovascular effects of novel S1P receptor agonists.

Translational pharmacokinetic modeling of fingolimod (FTY720) as a paradigm compound subject to sphingosine kinase-mediated phosphorylation.

A complicating factor in the translational pharmacology of sphingosine 1-phosphate agonists is that they exert their pharmacological effect through their respective phosphate metabolites, which are formed by the enzyme sphingosine kinase (S1PHK). In this investigation, we present a semimechanistic pharmacokinetic model for the interconversion of S1PHK substrates and their respective phosphates in rats and humans with the aim of investigating whether characterization of the rate of phosphorylation in blood platelets constitutes a basis for interspecies scaling using fingolimod as a model compound. Data on the time course of fingolimod and fingolimod-phosphate (fingolimod-P) blood concentrations after intravenous and oral administration of fingolimod and/or fingolimod-P in rats and after oral administration of fingolimod in doses of 0.5, 1.25, and 5 mg once daily in healthy volunteers were analyzed in conjunction with data on the ex vivo interconversion and blood-plasma distribution in rat and human blood, respectively. Integrating the data from the ex vivo and in vivo studies enabled simulation of fingolimod and fingolimod-P concentrations in plasma rather than blood, which are more relevant for characterizing drug effects. Large interspecies differences in the rate of phosphorylation between rats and humans were quantified. In human, phosphorylation of fingolimod in the platelets was four times slower compared with rat, whereas the dephosphorylation rates were comparable in both species. This partly explained the 10-12-fold overprediction of fingolimod-P exposure in human when applying a dose-by-factor approach on the developed rat model. Additionally, differences in presystemic phosphorylation should also be taken into account.

Characterization of the bronchodilatory dose response to indacaterol in patients with chronic obstructive pulmonary disease using model-based approaches.

BACKGROUND: Indacaterol is a once-daily long-acting inhaled ß2-agonist indicated for maintenance treatment of moderate-to-severe chronic obstructive pulmonary disease (COPD). The large inter-patient and inter-study variability in forced expiratory volume in 1 second (FEV1) with bronchodilators makes determination of optimal doses difficult in conventional dose-ranging studies. We considered alternative methods of analysis. METHODS: We utilized a novel modelling approach to provide a robust analysis of the bronchodilatory dose response to indacaterol. This involved pooled analysis of study-level data to characterize the bronchodilatory dose response, and nonlinear mixed-effects analysis of patient-level data to characterize the impact of baseline covariates. RESULTS: The study-level analysis pooled summary statistics for each steady-state visit in 11 placebo-controlled studies. These study-level summaries encompassed data from 7476 patients at indacaterol doses of 18.75-600 µg once daily, and showed that doses of 75 µg and above achieved clinically important improvements in predicted trough FEV1 response. Indacaterol 75 µg achieved 74% of the maximum effect on trough FEV1, and exceeded the midpoint of the 100-140 mL range that represents the minimal clinically important difference (MCID; ≥120 mL vs placebo), with a 90% probability that the mean improvement vs placebo exceeded the MCID. Indacaterol 150 µg achieved 85% of the model-predicted maximum effect on trough FEV1 and was numerically superior to all comparators (99.9% probability of exceeding MCID). Indacaterol 300 µg was the lowest dose that achieved the model-predicted maximum trough response.The patient-level analysis included data from 1835 patients from two dose-ranging studies of indacaterol 18.75-600 µg once daily. This analysis provided a characterization of dose response consistent with the study-level analysis, and demonstrated that disease severity, as captured by baseline FEV1, significantly affects the dose response, indicating that patients with more severe COPD require higher doses to achieve optimal bronchodilation. CONCLUSIONS: Comprehensive assessment of the bronchodilatory dose response of indacaterol in COPD patients provided a robust confirmation that 75 µg is the minimum effective dose, and that 150 and 300 µg are expected to provide optimal bronchodilation, particularly in patients with severe disease.

Fostering culture and optimizing organizational structure for implementing model-based drug development.

Model-based drug development (MBDD) is a promising approach to improve decision making in drug development. The pharmaceutical industry has made substantial progress from engaging in empirical decision making to increasingly using pharmacometrics (ie, modeling and simulation [M&S]) as a quantitative decision-making tool. Focusing on culture and an organizational structure perspective, this commentary summarizes experiences and vision from industry M&S leaders on implementing MBDD. A culture for MBDD needs to have wide acceptance of MBDD, enhanced decision making with probability-based evidence and transparent rationale, quantitative impact metrics, and a brand that emphasizes cross-disciplinary collaboration and ownership. An organizational structure for MBDD needs to have a dedicated pharmacometrics function, fine balance between quick wins and impact on long-term R&D goals, and collaborative MBDD teams among clinical pharmacologists, statisticians, pharmacometricians, and clinicians. Pharmaceutical companies with these characteristics are prepared to fully embrace and implement MBDD.

Enriched analgesic efficacy studies: an assessment by clinical trial simulation.

BACKGROUND AND OBJECTIVE: Enrichment strategies which select subjects who appear to respond to the drug have been used in drug studies to demonstrate clinical efficacy. We have used clinical trial simulation techniques to examine factors that are relevant in clinical trial design based on enrichment where poor responders are excluded from the double-blind phase of the study. METHODS: Simulations were performed for an analgesic trial design involving an open-dose titration phase (enrichment phase) followed by a double-blind, randomized, placebo-controlled maintenance phase. Enrichment was examined by excluding subjects above a predefined pain score (cutoff) from analysis of efficacy for the maintenance phase. Cutoff pain scores ranging from 4 to 7 on a 0 to 10 categorical scale were examined. A database consisting of chronic pain patients who participated in studies with a new formulation of buprenorphine was used to build the simulation model. Since no data were available for the key model variable "correlation between treatment and placebo response", values of 0.25, 0.5, and 0.75 were used for the simulations. RESULTS: A correlation between treatment and placebo effect ranging from 0.75 to 0.25 will cause the likelihood of trial success to vary from 50% to 95%. This model also shows that recruitment efficiency will decrease with the use of lower cutoff pain scores. CONCLUSION: Prior to using enrichment techniques, investigators must consider the correlation between treatment effect and placebo response to optimize clinical trial design.

Getting the Dose Right: report from the Tenth European Federation of Pharmaceutical Sciences (EUFEPS) conference on optimizing drug development.

This report highlights the main points emerging from a meeting sponsored on "Getting the Dose Right" in clinical development, jointly sponsored by the European Federation of Pharmaceutical Sciences and the European Center of Pharmaceutical Medicine, as part of the Workshop Series on Frontiers in Drug Development, in Basel, Switzerland on December 9-12, 2002.

Input characteristics and bioavailability after administration of immediate and a new extended-release formulation of hydromorphone in healthy volunteers.

BACKGROUND: To compare the pharmacokinetics of intravenous, oral immediate-release (IR), and oral extended-release (OROS ) formulations of hydromorphone. METHODS: In this randomized, six-session, crossover-design study, 12 subjects received hydromorphone 8-mg intravenous, 8-mg IR oral, and 8-, 16-, and 32-mg OROS formulations or placebo orally followed by plasma sampling for hydromorphone determination. Pharmacokinetic analysis was performed using NONMEM. Using the disposition of hydromorphone from the intravenous administration, deconvolution was used to estimate the input rate function (release rate from the gut to the blood) for the IR and OROS formulations. A linear spline was used to describe the drug input rate function. RESULTS: The deconvolution using linear splines described the release characteristics of both the IR and OROS formulations. The mean absolute bioavailability for the 8-mg OROS formulation was significantly larger ( = 0.025) than for the 8-mg IR formulation: 0.24 (SD 0.059) versus 0.19 (SD 0.054), respectively. The bioavailability was the same for the three doses of the OROS formulation. Predicted degree of fluctuation of plasma concentrations would be expected to be 130% and 39% for the IR and OROS 8-mg doses, respectively. CONCLUSIONS: The OROS formulation of hydromorphone produced continued release of medication over 24 h, which should allow for once-daily oral dosing. The extended release of hydromorphone will produce less fluctuation of plasma concentrations compared with IR formulations, which should provide for more constant pain control. The in vivo release of hydromorphone from both IR and OROS formulations were adequately described using a linear spline deconvolution approach. The increased bioavailability from the OROS formulation may be related to decreased metabolism by a first-pass effect or enterohepatic recycling of hydromorphone.