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.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet.

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.

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.

Exposure-Response Analyses Supporting Ticagrelor Dosing Recommendation in Patients With Prior Myocardial Infarction.

The relationships between drug exposure and the composite risk of cardiovascular (CV) death, myocardial infarction (MI), and stroke as well as the risk of TIMI major bleeding were estimated following long-term treatment with ticagrelor 60 or 90 mg twice daily in 20,942 patients with prior MI. These analyses support the primary reported efficacy and safety evaluations by showing that there were clear separations from placebo early in treatment with both doses, regardless of ticagrelor exposure, for both endpoints. In addition, the exposure-response analyses provided new insight into the contribution of individual exposure levels, rather than dose, as a predictor of events and accounted for differences in the baseline risk between patients. The predicted risks of CV death/MI/stroke were similar despite an increase in the median predicted ticagrelor average steady-state concentration from 606 nmol/L with ticagrelor 60 mg to 998 nmol/L with ticagrelor 90 mg (hazard ratios vs placebo of 0.83 and 0.81, respectively). The corresponding predicted risk of TIMI major bleeding slightly increased (hazard ratios vs placebo of 2.4 and 2.6, respectively). Apart from Japanese patients, showing a lower risk of CV death/MI/stroke, the response to ticagrelor was consistent across the study population, as supported by the combination of relatively flat exposure-response relationships in the studied exposure range, similar sensitivity to ticagrelor exposure, and small exposure differences. Consequently, the present analyses support the selection of the 60-mg dose for all demographic subgroups of patients studied.

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.

Innovative approaches to clinical development and trial design.

Pharmaceutical innovation is increasingly risky, costly and at times inefficient, which has led to a decline in industry productivity. Despite the increased investment in R&D by the industry, the number of new molecular entities achieving marketing authorization is not increasing. Novel approaches to clinical development and trial design could have a key role in overcoming some of these challenges by improving efficiency and reducing attrition rates. The effectiveness of clinical development can be improved by adopting a more integrated model that increases flexibility and maximizes the use of accumulated knowledge. Central to this model of drug development are novel tools, including modelling and simulation, Bayesian methodologies, and adaptive designs, such as seamless adaptive designs and sample-size re-estimation methods. Applications of these methodologies to early- and late-stage drug development are described with some specific examples, along with advantages, challenges, and barriers to implementation. Because they are so flexible, these new trial designs require significant statistical analyses, simulations and logistical considerations to verify their operating characteristics, and therefore tend to require more time for the planning and protocol development phase. Greater awareness of the distinct advantages of innovative designs by regulators and sponsors are crucial to increasing the adoption of these modern tools.

Understanding pharmacokinetics using realistic computational models of fluid dynamics: biosimulation of drug distribution within the CSF space for intrathecal drugs.

We introduce how biophysical modeling in pharmaceutical research and development, combining physiological observations at the tissue, organ and system level with selected drug physiochemical properties, may contribute to a greater and non-intuitive understanding of drug pharmacokinetics and therapeutic design. Based on rich first-principle knowledge combined with experimental data at both conception and calibration stages, and leveraging our insights on disease processes and drug pharmacology, biophysical modeling may provide a novel and unique opportunity to interactively characterize detailed drug transport, distribution, and subsequent therapeutic effects. This innovative approach is exemplified through a three-dimensional (3D) computational fluid dynamics model of the spinal canal motivated by questions arising during pharmaceutical development of one molecular therapy for spinal cord injury. The model was based on actual geometry reconstructed from magnetic resonance imaging data subsequently transformed in a parametric 3D geometry and a corresponding finite-volume representation. With dynamics controlled by transient Navier-Stokes equations, the model was implemented in a commercial multi-physics software environment established in the automotive and aerospace industries. While predictions were performed in silico, the underlying biophysical models relied on multiple sources of experimental data and knowledge from scientific literature. The results have provided insights into the primary factors that can influence the intrathecal distribution of drug after lumbar administration. This example illustrates how the approach connects the causal chain underlying drug distribution, starting with the technical aspect of drug delivery systems, through physiology-driven drug transport, then eventually linking to tissue penetration, binding, residence, and ultimately clearance. Currently supporting our drug development projects with an improved understanding of systems physiology, biophysical models are being increasingly used to characterize drug transport and distribution in human tissues where pharmacokinetic measurements are difficult or impossible to perform. Importantly, biophysical models can describe emergent properties of a system, i.e. properties not identifiable through the study of the system's components taken in isolation.

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.

The future of drug development: advancing clinical trial design.

Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools--such as Bayesian methodologies--in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.

Current issues relating to drug safety especially with regard to the use of biomarkers: a meeting report and progress update.

An expert group met in Leiden to review the state-of-the-art in detecting drug-related safety problems and to review the role of biomarkers and modelling techniques. It was clear that new drugs are not necessarily safer than old drugs, despite much larger clinical trial programs. Larger or longer clinical trials may be unfeasible and postmarketing surveillance is not being undertaken systematically enough to ensure safety. Biomarkers could improve drug safety by detecting drug-related signals early but determining whether the biomarker is on the causal pathway to toxicity is difficult. The maturity and utility of safety-related biomarkers varies among target organ systems. A consortium approach to assimilate a large amount of biomarker-related safety signals incorporating this information into mechanism-based models may provide a useful way forward. However no techniques will ensure that drugs are perfectly safe and communication with the public is required to achieve mutual understanding of benefit risk/balance assessments.

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.