Development and Validation of an In Silico Decision Tool To Guide Optimization of Intravenous Artesunate Dosing Regimens for Severe Falciparum Malaria Patients

Most deaths from severe falciparum malaria occur within 24 h of presentation to a hospital. Intravenous (i.v.) artesunate is the first-line treatment for severe falciparum malaria, but its efficacy may be compromised by delayed parasitological responses. In patients with severe malaria, the life-saving benefit of the artemisinin derivatives is their ability to clear circulating parasites rapidly, before they can sequester and obstruct the microcirculation. To evaluate the dosing of i.v. artesunate for the treatment of artemisinin-sensitive and reduced ring stage sensitivity to artemisinin severe falciparum malaria infections, Bayesian pharmacokinetic-pharmacodynamic modeling of data from 94 patients with severe malaria (80 children from Africa and 14 adults from Southeast Asia) was performed. Assuming that delayed parasite clearance reflects a loss of ring stage sensitivity to artemisinin derivatives, the median (95% credible interval) percentage of patients clearing ≥99% of parasites within 24 h (PC24≥99%) for standard (2.4 mg/kg body weight i.v. artesunate at 0 and 12 h) and simplified (4 mg/kg i.v. artesunate at 0 h) regimens was 65% (52.5% to 74.5%) versus 44% (25% to 61.5%) for adults, 62% (51.5% to 74.5%) versus 39% (20.5% to 58.5%) for larger children (≥20 kg), and 60% (48.5% to 70%) versus 36% (20% to 53.5%) for smaller children (<20 kg). The upper limit of the credible intervals for all regimens was below a PC24≥99% of 80%, a threshold achieved on average in clinical studies of severe falciparum malaria infections. In severe falciparum malaria caused by parasites with reduced ring stage susceptibility to artemisinin, parasite clearance is predicted to be slower with both the currently recommended and proposed simplified i.v. artesunate dosing regimens.

Pharmacokinetic and Pharmacodynamic Modeling of Tezepelumab to Guide Phase 3 Dose Selection for Patients With Severe Asthma

Tezepelumab is a human monoclonal antibody that blocks thymic stromal lymphopoietin, an epithelial cytokine involved in asthma pathogenesis. In the phase 2b PATHWAY study (ClinicalTrials.gov identifier: NCT02054130), tezepelumab significantly reduced exacerbations in adults with severe, uncontrolled asthma. We used pharmacokinetic (PK) and pharmacodynamic (PD) modeling to guide tezepelumab dose selection for phase 3 trials in patients with severe asthma. PK data from 7 clinical studies were used to develop a population PK model. Population PK-PD models were developed to characterize the relationship between tezepelumab PK and asthma exacerbation rate (AER) and fractional exhaled nitric oxide (FeNO) levels (using phase 2b PD data only). Tezepelumab PK were well described by a 2-compartment model with first-order absorption; PK parameter estimates were consistent with those of other immunoglobulin G2 antibodies. PK-PD models predicted that subcutaneous dosing at 210 mg every 4 weeks was associated with ≈90% of the maximum drug effect of tezepelumab on AER and FeNO; further dose increases were not expected to result in additional, clinically meaningful treatment benefit. No clinically significant covariates of treatment effects on AER and FeNO were identified. Population PK simulations, exposure-response relationships and safety profiles of tezepelumab at doses up to 280 mg every 2 weeks suggested that no dose adjustment based on body weight or for adolescents was required. These results support the selection of 210 mg every 4 weeks subcutaneously as the dose for phase 3 studies of tezepelumab in adults and adolescents with severe asthma.

Population Pharmacodynamic Modeling Using the Sigmoid Emax Model: Influence of Inter-individual Variability on the Steepness of the Concentration-Effect Relationship. a Simulation Study

The relationship between the concentration of a drug and its pharmacological effect is often described by empirical mathematical models. We investigated the relationship between the steepness of the concentration–effect relationship and inter-individual variability (IIV) of the parameters of the sigmoid E_max model, using the similarity between the sigmoid E_max model and the cumulative log-normal distribution. In addition, it is investigated whether IIV in the model parameters can be estimated accurately by population modeling. Multiple data sets, consisting of 40 individuals with 4 binary observations in each individual, were simulated with varying values for the model parameters and their IIV. The data sets were analyzed using Excel Solver and NONMEM. An empirical equation (Eq. (11)) was derived describing the steepness of the population-predicted concentration–effect profile (γ*) as a function of γ and IIV in C50 and γ, and was validated for both binary and continuous data. The tested study design is not suited to estimate the IIV in C50 and γ with reasonable precision. Using a naive pooling procedure, the population estimates γ* are significantly lower than the value of γ used for simulation. The steepness of the population-predicted concentration–effect relationship (γ*) is less than that of the individuals (γ). Using γ*, the population-predicted drug effect represents the drug effect, for binary data the probability of drug effect, at a given concentration for an arbitrary individual.

Dopamine Buffering Capacity Imaging: A Pharmacodynamic fMRI Method for Staging Parkinson Disease

We propose a novel pharmacological fMRI (phMRI) method for objectively quantifying disease severity in Parkinson disease (PD). It is based on the clinical observation that the benefit from a dose of levodopa wears off more quickly as PD progresses. Biologically this has been thought to represent decreased buffering capacity for dopamine as nigrostriatal cells die. Buffering capacity has been modeled based on clinical effects, but clinical measurements are influenced by confounding factors. The new method proposes to measure the effect objectively based on the timing of the known response of several brain regions to exogenous levodopa. Such responses are robust and can be quantified using perfusion MRI. Here we present simulation studies based on published clinical dose-response data and an intravenous levodopa infusion. Standard pharmacokinetic-pharmacodynamic methods were used to model the response. Then the effect site rate constant k e was estimated from simulated response data plus Gaussian noise. Predicted time - effect curves sampled at times consistent with phMRI differ substantially based on clinical severity. Estimated k e from noisy input data was recovered with good accuracy. These simulation results support the feasibility of levodopa phMRI hysteresis mapping to measure the severity of dopamine denervation objectively and simultaneously in all brain regions with a robust imaging response to exogenous levodopa.

In Silico Investigation of the Decline in Clinical Efficacy of Artemisinin Combination Therapies Due to Increasing Artemisinin and Partner Drug Resistance

Antimalarial treatment currently relies on an artemisinin derivative and a longer-acting partner drug. With the emergence of resistance to the artemisinin derivatives and the potential pressure this exerts on the partner drugs, the impact of resistance to each drug on efficacy needs to be investigated. An in silico exploration of dihydroartemisinin-piperaquine and mefloquine-artesunate, two artemisinin-based combination therapies that are commonly used in Southeast Asia, was performed. The percentage of treatment failures was simulated from a within-host pharmacokinetic-pharmacodynamic (PKPD) model, assuming that parasites developed increasing levels of (i) artemisinin derivative resistance or (ii) concomitant resistance to both the artemisinin derivative and the partner drug. Because the exact nature of how resistant Plasmodium falciparum parasites respond to treatment is unknown, we examined the impact on treatment failure rates of artemisinin resistance that (i) reduced the maximal killing rate, (ii) increased the concentration of drug required for 50% killing, or (iii) shortened the window of parasite stages that were susceptible to artemisinin derivatives until the drugs had no effect on the ring stages. The loss of the ring-stage activity of the artemisinin derivative caused the greatest increase in the treatment failure rate, and this result held irrespective of whether partner drug resistance was assumed to be present or not. To capture the uncertainty regarding how artemisinin derivative and partner drug resistance affects the assumed concentration-killing effect relationship, a variety of changes to this relationship should be considered when using within-host PKPD models to simulate clinical outcomes to guide treatment strategies for resistant infections.

Translational modeling and simulation approaches for molecularly targeted small molecule anticancer agents from bench to bedside

INTRODUCTION

Recent advances in molecular biology have enabled personalized cancer therapies with molecularly targeted agents (MTAs), which offer a promising future for cancer therapy. Dynamic modeling and simulation (M&S) is a powerful mathematical approach linking drug exposures to pharmacological responses, providing a quantitative assessment of in vivo drug potency. Accordingly, a growing emphasis is being placed upon M&S to quantitatively understand therapeutic exposure-response relationships of MTAs in nonclinical models.

AREAS COVERED

An overview of M&S approaches for MTAs in nonclinical models is presented with discussion about mechanistic extrapolation of antitumor efficacy from bench to bedside. Emphasis is placed upon recent advances in M&S approaches linking drug exposures, biomarker responses (e.g. target modulation) and pharmacological outcomes (e.g. antitumor efficacy).

EXPERT OPINION

For successful personalized cancer therapies with MTAs, it is critical to mechanistically and quantitatively understand their exposure-response relationships in nonclinical models, and to logically and properly apply such knowledge to the clinic. Particularly, M&S approaches to predict pharmacologically active concentrations of MTAs in patients based upon nonclinical data would be highly valuable in guiding the design and execution of clinical trials. Proactive approaches to understand their exposure-response relationships could substantially increase probability of achieving a positive proof-of-concept in the clinic.

Tumor Static Concentration Curves in Combination Therapy

Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h^-1 and the natural cell death to 0.0039 h^-1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL^-1 and 56 ng · mL^-1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.

Quantitative measurement of the target-mediated internalization kinetics of biopharmaceuticals

PURPOSE

Measurement of internalization of biopharmaceuticals targeting cell surface proteins can greatly facilitate drug development. The objective of this study was to develop a reliable method for determination of internalization rate constant (kint) and to demonstrate its utility.

METHODS

This method utilized confocal imaging to record the internalization kinetics of fluorescence-tagged biopharmaceuticals in live-cells and a quantitative image-analysis algorithm for kint determination. Kint was incorporated into a pharmacokinetic-pharmacodynamic (PK-PD) model for simulation of the drug PK profiles, target occupancy and the displacement of endogenous ligand.

RESULTS

The method was highly sensitive, allowing kint determination in cells expressing as low as 5,000 receptors/cell, and was amenable to adherent and suspension cells. Its feasibility in a mixed cell population, such as whole blood, was also demonstrated. Accurate assessment of the kint was largely attributed to continuous monitoring of internalization in live cells, rapid confocal image acquisition and quantitative image-analysis algorithm. Translational PK-PD simulations demonstrated that kint is a major determinant of the drug PK profiles, target occupancy, and the displacement of endogenous ligand.

CONCLUSIONS

The developed method is robust for broad cell types. Reliable kint assessment can greatly expedite biopharmaceutical development by facilitating target evaluation, drug affinity goal setting, and clinical dose projection.