A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development.

Historically, vaccine development and dose optimization have followed mostly empirical approaches without clinical pharmacology and model-informed approaches playing a major role, in contrast to conventional drug development. This is attributed to the complex cascade of immunobiological mechanisms associated with vaccines and a lack of quantitative frameworks for extracting dose-exposure-efficacy-toxicity relationships. However, the Covid-19 pandemic highlighted the lack of sufficient immunogenicity due to suboptimal vaccine dosing regimens and the need for well-designed, model-informed clinical trials which enhance the probability of selection of optimal vaccine dosing regimens. In this perspective, we attempt to develop a quantitative clinical pharmacology-based approach that integrates vaccine dose-efficacy-toxicity across various stages of vaccine development into a unified framework that we term as model-informed vaccine dose-optimization and development (MIVD). We highlight scenarios where the adoption of MIVD approaches may have a strategic advantage compared to conventional practices for vaccines.

Evaluation and Application of a PET Tracer in Preclinical and Phase 1 Studies to Determine the Brain Biodistribution of Minzasolmin (UCB0599).

PURPOSE: Minzasolmin (UCB0599) is an orally administered, small molecule inhibitor of ASYN misfolding in development as a potential disease-modifying therapy for Parkinson's disease. Here we describe the preclinical development of a radiolabeled tracer and results from a phase 1 study using the tracer to investigate the brain distribution of minzasolmin. PROCEDURES: In the preclinical study, two radiolabeling positions were investigated on the S-enantiomer of minzasolmin (UCB2713): [11C]methylamine UCB2713 ([11C-N-CH3]UCB2713) and [11C]carbonyl UCB2713 ([11C-CO]UCB2713). Male C57 black 6 mice (N = 10) received intravenous [11C-N-CH3]UCB2713; brain homogenates were assessed for radioactivity and plasma samples analyzed by high-performance liquid chromatography. Positron emission tomography-computed tomography (PET-CT) was used to image brains in a subset of mice (n = 3). In the open-label, phase 1 study, healthy volunteers were scanned twice with PET-CT following injection with [11C]minzasolmin radiotracer (≤ 10 µg), first without, then with oral dosing with non-radiolabeled minzasolmin 360 mg. PRIMARY OBJECTIVE: to determine biodistribution of minzasolmin in the human brain; secondary objectives included minzasolmin safety/tolerability. RESULTS: Preclinical data supported the use of [11C]minzasolmin in clinical studies. In the phase 1 study, PET data showed substantial drug signal in the brain of healthy volunteers (N = 4). The mean estimated whole brain total distribution volume (VT) at equilibrium across all regions of interest was 0.512 mL/cm3, no difference in VT was observed following administration of minzasolmin 360 mg. Treatment-emergent adverse events (TEAEs) were reported by 75% (n = 3) of participants. No drug-related TEAEs, deaths, serious adverse events, or discontinuations were reported. CONCLUSION: Following positive preclinical results with the N-methyl labeled PET tracer, [11C]minzasolmin was used in the phase 1 study, which demonstrated that minzasolmin readily crossed the blood-brain barrier and was well distributed throughout the brain. Safety and pharmacokinetic findings were consistent with previous early-phase studies (such as UP0077, NCT04875962).

Population pharmacokinetic-pharmacodynamic analysis of givinostat.

BACKGROUND: Givinostat (ITF2357), an oral, synthetic histone deacetylase inhibitor, significantly improved all histological muscle biopsy parameters in a Phase II study in boys with Duchenne muscular dystrophy (DMD). RESEARCH DESIGN AND METHODS: A population pharmacokinetic (PK) model, including seven clinical studies, was developed to explore the effect of covariates on givinostat PK. The final model was qualified to simulate pediatric dosing recommendations. A PK/pharmacodynamic (PD) model was developed to simulate the link between givinostat plasma concentration and platelet time course in 10-70-kg children following 6 months of givinostat 20-70 mg twice daily. RESULTS: A two-compartment model, with first-order input with lag and first-order elimination from the central compartment, described givinostat PK, demonstrating increasing apparent clearance with increasing body weight. The PK/PD model well-described platelet count time course. Weight-based dosing (arithmetic mean systemic exposure of 554-641 ng·h/mL) produced an average platelet count decrease from baseline of 45% with maximum decrease within 28 days. After 1 week and 6 months, ~1% and ~14-15% of patients, respectively, had a platelet count <75 × 109/L. CONCLUSIONS: Based on these data, givinostat dosing will be body weight adjusted and include monitoring of platelet counts to support efficacy and safety in a Phase III DMD study.

Phase 1/1b Studies of UCB0599, an Oral Inhibitor of α-Synuclein Misfolding, Including a Randomized Study in Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) and its progression are thought to be caused and driven by misfolding of α-synuclein (ASYN). UCB0599 is an oral, small-molecule inhibitor of ASYN misfolding, aimed at slowing disease progression. OBJECTIVE: The aim was to investigate safety/tolerability and pharmacokinetics (PK) of single and multiple doses of UCB0599. METHODS: Safety/tolerability and PK of single and multiple doses of UCB0599 and its metabolites were investigated in two phase 1 studies in healthy participants (HPs), where food effect and possible interaction with itraconazole (ITZ) were assessed (UP0030 [randomized, placebo-controlled, dose-escalation, crossover study, N = 65] and UP0078 [open-label study, N = 22]). Safety/tolerability and multi-dose PK of UCB0599 were subsequently investigated in a phase 1b randomized, double-blind, placebo-controlled study of participants with PD (UP0077 [NCT04875962], N = 31). RESULTS: Across all studies, UCB0599 displayed rapid absorption with linear, time-independent PK properties; PK of multiple doses of UCB0599 were predictable from single-dose exposures. No notable food-effect was observed; co-administration with ITZ affected UCB0599 disposition (maximum plasma concentration and area under the curve increased ~1.3- and ~2 to 3-fold, respectively) however, this did not impact the safety profile. Hypersensitivity reactions were reported in UP0030 (n = 2) and UP0077 (n = 2). Treatment-related adverse events occurred in 43% (UCB0599), and 30% (placebo) of participants with PD were predominantly mild-to-moderate in intensity and were not dose related. CONCLUSIONS: Seventy-three HPs and 21 participants with PD received UCB0599 doses; an acceptable safety/tolerability profile and predictable PK support continued development of UCB0599 for the slowing of PD progression. A phase 2 study in early-stage PD is underway (NCT04658186). © 2022 UCB Pharma. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Padsevonil randomized Phase IIa trial in treatment-resistant focal epilepsy: a translational approach.

Therapeutic options for patients with treatment-resistant epilepsy represent an important unmet need. Addressing this unmet need was the main factor driving the drug discovery program that led to the synthesis of padsevonil, a first-in-class antiepileptic drug candidate that interacts with two therapeutic targets: synaptic vesicle protein 2 and GABAA receptors. Two PET imaging studies were conducted in healthy volunteers to identify optimal padsevonil target occupancy corresponding to levels associated with effective antiseizure activity in rodent models. Optimal padsevonil occupancy associated with non-clinical efficacy was translatable to humans for both molecular targets: high (>90%), sustained synaptic vesicle protein 2A occupancy and 10-15% transient GABAA receptor occupancy. Rational dose selection enabled clinical evaluation of padsevonil in a Phase IIa proof-of-concept trial (NCT02495844), with a single-dose arm (400 mg bid). Adults with highly treatment-resistant epilepsy, who were experiencing ≥4 focal seizures/week, and had failed to respond to ≥4 antiepileptic drugs, were randomized to receive placebo or padsevonil as add-on to their stable regimen. After a 3-week inpatient double-blind period, all patients received padsevonil during an 8-week outpatient open-label period. The primary endpoint was ≥75% reduction in seizure frequency. Of 55 patients randomized, 50 completed the trial (placebo n = 26; padsevonil n = 24). Their median age was 36 years (range 18-60), and they had been living with epilepsy for an average of 25 years. They were experiencing a median of 10 seizures/week and 75% had failed ≥8 antiepileptic drugs. At the end of the inpatient period, 30.8% of patients on padsevonil and 11.1% on placebo were ≥75% responders (odds ratio 4.14; P = 0.067). Reduction in median weekly seizure frequency was 53.7% and 12.5% with padsevonil and placebo, respectively (unadjusted P = 0.026). At the end of the outpatient period, 31.4% were ≥75% responders and reduction in median seizure frequency was 55.2% (all patients). During the inpatient period, 63.0% of patients on placebo and 85.7% on padsevonil reported treatment-emergent adverse events. Overall, 50 (90.9%) patients who received padsevonil reported treatment-emergent adverse events, most frequently somnolence (45.5%), dizziness (43.6%) and headache (25.5%); only one patient discontinued due to a treatment-emergent adverse event. Padsevonil was associated with a favourable safety profile and displayed clinically meaningful efficacy in patients with treatment-resistant epilepsy. The novel translational approach and the innovative proof-of-concept trial design maximized signal detection in a small patient population in a short duration, expediting antiepileptic drug development for the population with the greatest unmet need in epilepsy.

A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination.

PURPOSE: In clinical oncology, combination treatments are widely used and increasingly preferred over single drug administrations. A better characterization of the interaction between drug effects and the selection of synergistic combinations represent an open challenge in drug development process. To this aim, preclinical studies are routinely performed, even if they are only qualitatively analyzed due to the lack of generally applicable mathematical models. METHODS: This paper presents a new pharmacokinetic-pharmacodynamic model that, starting from the well-known single agent Simeoni TGI model, is able to describe tumor growth in xenograft mice after the co-administration of two anticancer agents. Due to the drug action, tumor cells are divided in two groups: damaged and not damaged ones. The damaging rate has two terms proportional to drug concentrations (as in the single drug administration model) and one interaction term proportional to their product. Six of the eight pharmacodynamic parameters assume the same value as in the corresponding single drug models. Only one parameter summarizes the interaction, and it can be used to compute two important indexes that are a clear way to score the synergistic/antagonistic interaction among drug effects. RESULTS: The model was successfully applied to four new compounds co-administered with four drugs already available on the market for the treatment of three different tumor cell lines. It also provided reliable predictions of different combination regimens in which the same drugs were administered at different doses/schedules. CONCLUSIONS: A good and quantitative measurement of the intensity and nature of interaction between drug effects, as well as the capability to correctly predict new combination arms, suggest the use of this generally applicable model for supporting the experiment optimal design and the prioritization of different therapies.

NMS-E973, a novel synthetic inhibitor of Hsp90 with activity against multiple models of drug resistance to targeted agents, including intracranial metastases.

PURPOSE: Recent developments of second generation Hsp90 inhibitors suggested a potential for development of this class of molecules also in tumors that have become resistant to molecular targeted agents. Disease progression is often due to brain metastases, sometimes related to insufficient drug concentrations within the brain. Our objective was to identify and characterize a novel inhibitor of Hsp90 able to cross the blood-brain barrier (BBB). EXPERIMENTAL DESIGN: Here is described a detailed biochemical and crystallographic characterization of NMS-E973. Mechanism-based anticancer activity was described in cell models, including models of resistance to kinase inhibitors. Pharmacokinetics properties were followed in plasma, tumor, liver, and brain. In vivo activity and pharmacodynamics, as well as the pharmacokinetic/pharmacodynamic relationships, were evaluated in xenografts, including an intracranially implanted melanoma model. RESULTS: NMS-E973, representative of a novel isoxazole-derived class of Hsp90 inhibitors, binds Hsp90α with subnanomolar affinity and high selectivity towards kinases, as well as other ATPases. It possesses potent antiproliferative activity against tumor cell lines and a favorable pharmacokinetic profile, with selective retention in tumor tissue and ability to cross the BBB. NMS-E973 induces tumor shrinkage in different human tumor xenografts, and is highly active in models of resistance to kinase inhibitors. Moreover, consistent with its brain penetration, NMS-E973 is active also in an intracranially implanted melanoma model. CONCLUSIONS: Overall, the efficacy profile of NMS-E973 suggests a potential for development in different clinical settings, including tumors that have become resistant to molecular targeted agents, particularly in cases of tumors which reside beyond the BBB.

Preclinical studies in the mdx mouse model of duchenne muscular dystrophy with the histone deacetylase inhibitor givinostat.

Previous work has established the existence of dystrophin-nitric oxide (NO) signaling to histone deacetylases (HDACs) that is deregulated in dystrophic muscles. As such, pharmacological interventions that target HDACs (that is, HDAC inhibitors) are of potential therapeutic interest for the treatment of muscular dystrophies. In this study, we explored the effectiveness of long-term treatment with different doses of the HDAC inhibitor givinostat in mdx mice--the mouse model of Duchenne muscular dystrophy (DMD). This study identified an efficacy for recovering functional and histological parameters within a window between 5 and 10 mg/kg/d of givinostat, with evident reduction of the beneficial effects with 1 mg/kg/d dosage. The long-term (3.5 months) exposure of 1.5-month-old mdx mice to optimal concentrations of givinostat promoted the formation of muscles with increased cross-sectional area and reduced fibrotic scars and fatty infiltration, leading to an overall improvement of endurance performance in treadmill tests and increased membrane stability. Interestingly, a reduced inflammatory infiltrate was observed in muscles of mdx mice exposed to 5 and 10 mg/kg/d of givinostat. A parallel pharmacokinetic/pharmacodynamic analysis confirmed the relationship between the effective doses of givinostat and the drug distribution in muscles and blood of treated mice. These findings provide the preclinical basis for an immediate translation of givinostat into clinical studies with DMD patients.

Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts.

PURPOSE: Pharmacokinetic-pharmacodynamic (PK-PD) models able to predict the action of anticancer compounds in tumor xenografts have an important impact on drug development. In case of anti-angiogenic compounds, many of the available models show difficulties in their applications, as they are based on a cell kill hypothesis, while these drugs act on the tumor vascularization, without a direct tumor cell kill effect. For this reason, a PK-PD model able to describe the tumor growth modulation following treatment with a cytostatic therapy, as opposed to a cytotoxic treatment, is proposed here. METHODS: Untreated tumor growth was described using an exponential growth phase followed by a linear one. The effect of anti-angiogenic compounds was implemented using an inhibitory effect on the growth function. The model was tested on a number of experiments in tumor-bearing mice given the anti-angiogenic drug bevacizumab either alone or in combination with another investigational compound. Nonlinear regression techniques were used for estimating the model parameters. RESULTS: The model successfully captured the tumor growth data following different bevacizumab dosing regimens, allowing to estimate experiment-independent parameters. A combination model was also developed under a 'no-interaction' assumption to assess the effect of the combination of bevacizumab with a target-oriented agent. The observation of a significant difference between model-predicted and observed tumor growth curves was suggestive of the presence of a pharmacological interaction that was further accommodated into the model. CONCLUSIONS: This approach can be used for optimizing the design of preclinical experiments. With all the inherent limitations, the estimated experiment-independent model parameters can be used to provide useful indications for the single-agent and combination regimens to be explored in the subsequent development phases.

A4S: a user-friendly graphical tool for pharmacokinetic and pharmacodynamic (PK/PD) simulation.

Effective communication of PK/PD principles and results in a biomedical research environment remains a significant challenge which can result in lack of buy-in and engagement from scientists outside the modeling and simulation communities. In our view, one of the barriers in this area is a lack of user-friendly tools which allow "non experts" to use PK/PD models without the need to develop technical skills and expertise in advanced mathematical principles and specialist software. The costs of commercial software may also prevent large-scale distribution. One attempt to address this issue internally in our research organizations has resulted in the development of the A4S ("Accelera for Sandwich") software, which is a simple-to-use, menu-drive Matlab-based PK/PD simulator targeted at biomedical researchers with little PK/PD experience.

A model-based approach to the in vitro evaluation of anticancer activity.

PURPOSE: The use of in vitro screening tests for characterizing the activity of anticancer agents is a standard practice in oncology research and development. In these studies, human A2780 ovarian carcinoma cells cultured in plates are exposed to different concentrations of the compounds for different periods of time. Their anticancer activity is then quantified in terms of EC(50) comparing the number of metabolically active cells present in the treated and the control arms at specified time points. The major concern of this methodology is the observed dependency of the EC(50) on the experimental design in terms of duration of exposure. This dependency could affect the efficacy ranking of the compounds, causing possible biases especially in the screening phase, when compound selection is the primary purpose of the in vitro analysis. To overcome this problem, the applicability of a modeling approach to these in vitro studies was evaluated. METHODS: The model, consisting of a system of ordinary differential equations, represents the growth of tumor cells using a few identifiable and biologically relevant parameters related to cell proliferation dynamics and drug action. In particular, the potency of the compounds can be measured by a unique and drug-specific parameter that is essentially independent of drug concentration and exposure time. Parameter values were estimated using weighted nonlinear least squares. RESULTS: The model was able to adequately describe the growth of tumor cells at different experimental conditions. The approach was validated both on commercial drugs and discovery candidate compounds. In addition, from this model the relationship between EC(50) and the exposure time was derived in an analytic form. CONCLUSIONS: The proposed approach provides a new tool for predicting and/or simulating cell responses to different treatments with useful indications for optimizing in vitro experimental designs. The estimated potency parameter values obtained from different compounds can be used for an immediate ranking of anticancer activity.

A minimal model of tumor growth inhibition.

The preclinical development of antitumor drugs greatly benefits from the availability of models capable of predicting tumor growth as a function of the drug administration schedule. For being of practical use, such models should be simple enough to be identifiable from standard experiments conducted on animals. In the present paper, a stochastic model is derived from a set of minimal assumptions formulated at cellular level. Tumor cells are divided in two groups: proliferating and nonproliferating. The probability that a proliferating cell generates a new cell is a function of the tumor weight. The probability that a proliferating cell becomes nonproliferating is a function of the plasma drug concentration. The time-to-death of a nonproliferating cell is a random variable whose distribution reflects the nondeterministic delay between drug action and cell death. The evolution of the expected value of tumor weight obeys two differential equations (an ordinary and a partial differential one), whereas the variance is negligible. Therefore, the tumor growth dynamics can be well approximated by the deterministic evolution of its expected value. The tumor growth inhibition model, which is a lumped parameter model that in the last few years has been successfully applied to several antitumor drugs, is shown to be a special case of the minimal model presented here.

Evaluation of a basic physiologically based pharmacokinetic model for simulating the first-time-in-animal study.

The objective of this study was to evaluate a physiologically based pharmacokinetic (PBPK) approach for predicting the plasma concentration-time curves expected after intravenous administration of candidate drugs to rodents. The predictions were based on a small number of properties that were either calculated based on the structure of the candidate drug (octanol:water partition coefficient, ionization constant(s)) or obtained from the typical high-throughput screens implemented in the early drug discovery phases (fraction unbound in plasma and hepatic intrinsic clearance). The model was tested comparing the predicted and the observed pharmacokinetics of 45 molecules. This dataset included six known drugs and 39 drug candidates from different discovery programs, so that the performance of the model could be evaluated in a real discovery case scenario. The plasma concentration-time curves were predicted with good accuracy, the pharmacokinetic parameters being on average two- to three-fold of actual values. Multivariate analysis was used for identifying the candidate properties which were likely associated to biased predictions. The application of this approach was found useful for the prioritization of the in vivo pharmacokinetics screens and the design of the first-time-in-animal studies.

Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents.

The available mathematical models describing tumor growth and the effect of anticancer treatments on tumors in animals are of limited use within the drug industry. A simple and effective model would allow applying quantitative thinking to the preclinical development of oncology drugs. In this article, a minimal pharmacokinetic-pharmacodynamic model is presented, based on a system of ordinary differential equations that link the dosing regimen of a compound to the tumor growth in animal models. The growth of tumors in nontreated animals is described by an exponential growth followed by a linear growth. In treated animals, the tumor growth rate is decreased by a factor proportional to both drug concentration and number of proliferating tumor cells. A transit compartmental system is used to model the process of cell death, which occurs at later times. The parameters of the pharmacodynamic model are related to the growth characteristics of the tumor, to the drug potency, and to the kinetics of the tumor cell death. Therefore, such parameters can be used for ranking compounds based on their potency and for evaluating potential differences in the tumor cell death process. The model was extensively tested on discovery candidates and known anticancer drugs. It fitted well the experimental data, providing reliable parameter estimates. On the basis of the parameters estimated in a first experiment, the model successfully predicted the response of tumors exposed to drugs given at different dose levels and/or schedules. It is, thus, possible to use the model prospectively, optimizing the design of new experiments.

In vitro cell growth pharmacodynamic studies: a new nonparametric approach to determining the relative importance of drug concentration and treatment time.

PURPOSE: The effect of an anticancer treatment on tumor cell proliferation in vitro can be described as a three-dimensional surface where the inhibitory effect is related to drug concentration and treatment time. The analysis of this kind of response surface could provide critical information: for example, it could indicate whether a prolonged exposure to a low concentration of an anticancer agent will produce a different effect from exposure to higher concentrations for a shorter period of time. The parametric approach available in the literature was not flexible enough to accommodate the behavior of the response surface in some of the data sets collected as part of our research programs. Therefore, a new, general, nonparametric approach was developed. METHODS: The response surface of the inhibition of cell-based tumor growth was described using a radial basis function neural network (RBF-NN). The RBF-NN was trained using regularization theory, which provided the initialization of a constrained quadratic optimization algorithm that imposes monotonicity of the surface with respect to both concentration and exposure time. RESULTS: In the two analyzed cases (doxorubicin and flavopiridol), the proposed method was accurate and reliable in describing the inhibition surface of tumor cell growth as a function of drug concentration and exposure time. Residuals were small and unbiased. The new method improved on the parametric approach when the relative importance of drug concentration and exposure time in determining the overall effect was not constant across the experimental data. CONCLUSIONS: The proposed RBF-NN can be reliably applied for the analysis in cell-based tumor growth inhibition studies. This approach can be used for optimizing the administration regimens to be adopted in vivo. The use of this methodology can be easily extended to any cell-based experiment, in which the outcome can be seen as a function of two experimental variables.