Emerging Therapeutics, Technologies, and Drug Development Strategies to Address Patient Nonadherence and Improve Tuberculosis Treatment.

Imperfect medication adherence remains the biggest predictor of treatment failure for patients with tuberculosis. Missed doses during treatment lead to relapse, tuberculosis resistance, and further spread of disease. Understanding individual patient phenotypes, population pharmacokinetics, resistance development, drug distribution to tuberculosis lesions, and pharmacodynamics at the site of infection is necessary to fully measure the impact of adherence on patient outcomes. To decrease the impact of expected variabilityin drug intake on tuberculosis outcomes, an improvement in patient adherence and new forgiving regimens that protect against missed doses are needed. In this review, we summarize emerging technologies to improve medication adherence in clinical practice and provide suggestions on how digital adherence technologies can be incorporated in clinical trials and practice and the drug development pipeline that will lead to more forgiving regimens and benefit patients suffering from tuberculosis.

Characterizing HIV-Preventive, Plasma Tenofovir Concentrations-A Pooled Participant-level Data Analysis From Human Immunodeficiency Virus Preexposure Prophylaxis Clinical Trials.

BACKGROUND: Daily dosing of tenofovir disoproxil fumarate, with or without emtricitabine, has high efficacy in preventing human immunodeficiency virus (HIV) infection when individuals are adherent. The target protective plasma concentration of tenofovir (TFV), however, is not fully understood. The aim of this study is to estimate the protective TFV plasma concentration. METHODS: Participant data from TFV-based daily oral and topical active arms of phase 3 trials (iPrEx, VOICE, and Partners PrEP) were pooled (n = 2950). Individual specific risk scores (low and high risk) of acquiring HIV, based on an earlier placebo analysis, were created. Longitudinal TFV pharmacokinetics (PK), HIV outcome, individual risk scores and the effect of sex at birth data were integrated and analyzed using non-linear mixed effects models. RESULTS: Around 50% of the individuals were estimated to be adherent, which differed from self-reported adherence (∼90%) and large variation between longitudinal adherence patterns were identified. Following oral administration, the estimated protective TFV trough concentration was substantially higher in high-risk females (45.8 ng/mL) compared with high-risk males (16.1 ng/mL) and to low-risk individuals (∼7.5 ng/mL). Dosing simulations indicated that high-risk women require full adherence to maintain protective levels. CONCLUSIONS: Using the largest PK-HIV outcome database to date, we developed a population adherence-PK-risk-outcome model. Our results indicate that high-risk females need higher levels of plasma TFV to achieve HIV protection compared with males. HIV protection exceeds 90% in all populations if daily adherence is achieved.

Combination of Mycobacterium tuberculosis RS Ratio and CFU Improves the Ability of Murine Efficacy Experiments to Distinguish between Drug Treatments.

Murine tuberculosis drug efficacy studies have historically monitored bacterial burden based on CFU of Mycobacterium tuberculosis in lung homogenate. In an alternative approach, a recently described molecular pharmacodynamic marker called the RS ratio quantifies drug effect on a fundamental cellular process, ongoing rRNA synthesis. Here, we evaluated the ability of different pharmacodynamic markers to distinguish between treatments in three BALB/c mouse experiments at two institutions. We confirmed that different pharmacodynamic markers measure distinct biological responses. We found that a combination of pharmacodynamic markers distinguishes between treatments better than any single marker. The combination of the RS ratio with CFU showed the greatest ability to recapitulate the rank order of regimen treatment-shortening activity, providing proof of concept that simultaneous assessment of pharmacodynamic markers measuring different properties will enhance insight gained from animal models and accelerate development of new combination regimens. These results suggest potential for a new era in which antimicrobial therapies are evaluated not only on culture-based measures of bacterial burden but also on molecular assays that indicate how drugs impact the physiological state of the pathogen.

Pharmacokinetics, optimal dosing, and safety of linezolid in children with multidrug-resistant tuberculosis: Combined data from two prospective observational studies.

BACKGROUND: Linezolid is increasingly important for multidrug-resistant tuberculosis (MDR-TB) treatment. However, among children with MDR-TB, there are no linezolid pharmacokinetic data, and its adverse effects have not yet been prospectively described. We characterised the pharmacokinetics, safety, and optimal dose of linezolid in children treated for MDR-TB. METHODS AND FINDINGS: Children routinely treated for MDR-TB in 2 observational studies (2011-2015, 2016-2018) conducted at a single site in Cape Town, South Africa, underwent intensive pharmacokinetic sampling after either a single dose or multiple doses of linezolid (at steady state). Linezolid pharmacokinetic parameters, and their relationships with covariates of interest, were described using nonlinear mixed-effects modelling. Children receiving long-term linezolid as a component of their routine treatment had regular clinical and laboratory monitoring. Adverse events were assessed for severity and attribution to linezolid. The final population pharmacokinetic model was used to derive optimal weight-banded doses resulting in exposures in children approximating those in adults receiving once-daily linezolid 600 mg. Forty-eight children were included (mean age 5.9 years; range 0.6 to 15.3); 31 received a single dose of linezolid, and 17 received multiple doses. The final pharmacokinetic model consisted of a one-compartment model characterised by clearance (CL) and volume (V) parameters that included allometric scaling to account for weight; no other evaluated covariates contributed to the model. Linezolid exposures in this population were higher compared to exposures in adults who had received a 600 mg once-daily dose. Consequently simulated, weight-banded once-daily optimal doses for children were lower than those currently used for most weight bands. Ten of 17 children who were followed long term had a linezolid-related adverse event, including 5 with a grade 3 or 4 event, all anaemia. Adverse events resulted in linezolid dose reductions in 4, temporary interruptions in 5, and permanent discontinuation in 4 children. Limitations of the study include the lack of very young children (none below 6 months of age), the limited number who were HIV infected, and the modest number of children contributing to long-term safety data. CONCLUSIONS: Linezolid-related adverse effects were frequent and occasionally severe. Careful linezolid safety monitoring is required. Compared to doses currently used in children in many settings for MDR-TB treatment, lower doses may approximate current adult target exposures, might result in fewer adverse events, and should therefore be evaluated.

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts.

Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n = 28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day-1 No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.

Characterizing Gemcitabine Effects Administered as Single Agent or Combined with Carboplatin in Mice Pancreatic and Ovarian Cancer Xenografts: A Semimechanistic Pharmacokinetic/Pharmacodynamics Tumor Growth-Response Model.

In this work, a semimechanistic tumor growth-response model for gemcitabine in pancreatic (administered as single agent) and ovarian (given as single agent and in combination with carboplatin) cancer in mice was developed. Tumor profiles were obtained from nude mice, previously inoculated with KP4, ASPC1, MIA PACA2, PANC1 (pancreas), A2780, or SKOV3×luc (ovarian) cell lines, and then treated with different dosing schedules of gemcitabine and/or carboplatin. Data were fitted using the population approach with Nonlinear Mixed Effect Models 7.2. In addition to cell proliferation, the tumor progression model for both types of cancer incorporates a carrying capacity representing metabolite pool for DNA synthesis required to tumor growth. Analysis of data from the treated groups revealed that gemcitabine exerted its tumor effects by promoting apoptosis as well as decreasing the carrying capacity compartment. Pharmacodynamic parameters were cell-specific and overall had similar range values between cancer types. In pancreas, a linear model was used to describe both gemcitabine effects with parameter values between 3.26 × 10-2 and 4.2 × 10-1 L/(mg × d). In ovarian cancer, the apoptotic effect was driven by an EMAX model with an efficacy/potency ratio of 5.25-8.65 L/(mg × d). The contribution of carboplatin to tumor effects was lower than the response exerted by gemcitabine and was incorporated in the model as an inhibition of the carrying capacity. The model developed was consistent in its structure across different tumor cell lines and two tumor types where gemcitabine is approved. Simulation-based evaluation diagnostics showed that the model performed well in all experimental design scenarios, including dose, schedule, and tumor type.

Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment.

The aims of this study are (i) to develop a population pharmacokinetic/pharmacodynamic model of daptomycin in patients with normal and impaired renal function, and (ii) to establish the optimal dose recommendation of daptomycin in clinical practice. Several structural PK models including linear and non-linear binding kinetics were evaluated. Monte Carlo simulations were conducted with a fixed combination of creatinine clearance (30-90 mL/min/1.73 m2) and body weight (50-100 kg). The final dataset included 46 patients and 157 daptomycin observations. A two-compartment model with first-order peripheral distribution and elimination kinetics assuming non-linear protein-binding kinetics was selected. The bactericidal effect for Gram+ strains with MIC ≤ 0.5 mg/L could be achieved with 5-12 mg/kg daily daptomycin based on body weight and renal function. The administration of 10-17 mg/kg q48 h daptomycin allows to achieve bactericidal effect for Gram+ strains with MIC ≤ 1 mg/L. Four PK samples were selected as the optimal sampling strategy for an accurate AUC estimation. A quantitative framework has served to characterize the non-linear binding kinetics of daptomycin in patients with normal and impaired renal function. The impact of different dosing regimens on the efficacy and safety outcomes of daptomycin treatment based on the unbound exposure of daptomycin and individual patient characteristics has been evaluated.

Modeling the Probability of HIV Infection over Time in High-Risk Seronegative Participants Receiving Placebo in Five Randomized Double-Blind Placebo-Controlled HIV Pre-Exposure Prophylaxis Trials: A Patient-Level Pooled Analysis.

The World Health Organization recommends pre-exposure prophylaxis (PrEP) for individuals at substantial risk of HIV infection. The aim of this analysis is to quantify the individual risk of HIV infection over time, using a large database of high-risk individuals (n = 5583). We used data from placebo recipients in five phase III PrEP trials: iPrEx, conducted in men who have sex with men and transgender women; VOICE, conducted in young women at high sexual risk; Partners PrEP, conducted in HIV serodiscordant heterosexual couples; TDF2, conducted in high-risk heterosexual men and women; and BTS, conducted in persons who inject drugs. The probability of HIV infection over time was estimated using NONMEM7.4. We identified predictors of HIV risk and found a substantial difference in the risk of infection among and within trial populations, with each study including a mix of low, moderate, and high-risk individuals (p < 0.05). Persons who were female at birth were at a higher risk of HIV infection than people who were male at birth. Final models were integrated in a tool that can assess person-specific risk and simulate cumulative HIV risk over time. These models can be used to optimize future PrEP clinical trials by identifying potential participants at highest risk.

Clinical Pharmacokinetics of Oral Sodium Selenite and Dosing Implications in the Treatment of Patients with Metastatic Cancer.

BACKGROUND: Selenite is a radiosensitizer and inhibitor of androgen receptor expression and function. In a phase I study (NCT02184533) in 15 subjects with metastatic cancer receiving daily oral sodium selenite with palliative radiation therapy, disease stabilization was observed, as evidenced by tumor regression, marked reduction in pain symptoms, and decreased prostate-specific antigen levels (only patients with castrate-resistant prostate cancer). OBJECTIVE: The aim of this work was to characterize the pharmacokinetics of selenite to suggest dosing strategies and to propose a study design for further investigation. METHODS: With selenium plasma concentrations obtained from five dosing cohorts (5.5, 11, 16.5, 33, and 49.5 mg), a population pharmacokinetic model was constructed using NONMEM. The model described externally administered selenite (inorganic) with a baseline component for endogenous selenium levels. Using the pharmacokinetic model, simulations were performed to suggest dosing regimens that achieved in vitro target selenite levels, and optimal pharmacokinetic sampling times for a subsequent study were proposed using PopED. RESULTS: A one-compartment model characterized selenite pharmacokinetics. Parameter estimates were absorption rate constant (0.64 h-1), apparent clearance (1.58 L/h), apparent volume of distribution (42.3 L), and baseline selenium amount (5270 µg). A logarithmic function characterized the inverse relationship between dose level and bioavailability. Four regimens to reach in vitro target selenite levels were proposed: 33 mg daily, 16.5 mg twice daily (BID), 11 mg BID, and 5.5 mg thrice daily (TID). Optimal sampling times were 1, 2, 6, and 24 h. DISCUSSION: The population model described the pharmacokinetic data well. Three regimens (33 mg daily, 11 mg BID, 5.5 mg TID) achieved in vitro target selenite levels after one dose. The model and optimal sampling times may inform future studies evaluating the efficacy of selenite for metastatic cancer treatment.

Oxypurinol pharmacokinetics and pharmacodynamics in healthy volunteers: Influence of BCRP Q141K polymorphism and patient characteristics.

The missense variant, breast cancer resistance protein (BCRP) p.Q141K, which encodes a reduced function BCRP, has been linked to poor response to allopurinol. Using a multifaceted approach, we aimed to characterize the relationship(s) between BCRP p.Q141K, the pharmacokinetics (PK) and pharmacodynamics (PD) of oxypurinol (the active metabolite of allopurinol), and serum uric acid (SUA) levels. A prospective clinical study (NCT02956278) was conducted in which healthy volunteers were given a single oral dose of 300 mg allopurinol followed by intensive blood sampling. Data were analyzed using noncompartmental analysis and population PK/PD modeling. Additionally, electronic health records were analyzed to investigate whether clinical inhibitors of BCRP phenocopied the effects of the p.Q141K variant with respect to SUA. Subjects homozygous for p.Q141K had a longer half-life (34.2 ± 12.2 h vs. 19.1 ± 1.42 h) of oxypurinol. The PK/PD model showed that women had a 24.8% lower volume of distribution. Baseline SUA was affected by p.Q141K genotype and renal function; that is, it changed by 48.8% for every 1 mg/dl difference in serum creatinine. Real-world data analyses showed that patients prescribed clinical inhibitors of BCRP have higher SUA levels than those that have not been prescribed inhibitors of BCRP, consistent with the idea that BCRP inhibitors phenocopy the effects of p.Q141K on uric acid levels. This study identified important covariates of oxypurinol PK/PD that could affect its efficacy for the treatment of gout as well as a potential side effect of BCRP inhibitors on increasing uric acid levels, which has not been described previously.

A Mechanistic In Vivo/Ex Vivo Pharmacokinetic-Pharmacodynamic Model of Tenofovir for HIV Prevention.

Defining tissue and plasma-specific prophylactic drug concentrations is central to pre-exposure prophylaxis product development for sexual transmission of HIV-1. Pharmacokinetic (PK) data from study RMP-02/MTN-006 comparing single dose oral tenofovir disoproxil fumarate with single and multiple dose rectal tenofovir (TFV) gel administration in HIV-1 seronegative adults was used to construct a multicompartment plasma-rectal tissue population PK model for TFV and tenofovir-diphosphate (TFVdp) in plasma and rectal tissue. PK data were collected in five matrices: TFV (plasma, rectal tissue homogenate), TFVdp (peripheral blood mononuclear cells, rectal mononuclear cells (MMCs), rectal tissue homogenate). A viral growth compartment and a delayed effect compartment for p24 antigen expression measured from an ex vivo explant assay described HIV-1 infection and replication. Using a linear PK/pharmacodynamic model, MMC TFVdp levels over 9,000 fmol/million cells in the explant assay provided apparent viral replication suppression down to 1%. Parameters were estimated using NONMEM version 7.4.

Mycobacterium tuberculosis precursor rRNA as a measure of treatment-shortening activity of drugs and regimens.

There is urgent need for new drug regimens that more rapidly cure tuberculosis (TB). Existing TB drugs and regimens vary in treatment-shortening activity, but the molecular basis of these differences is unclear, and no existing assay directly quantifies the ability of a drug or regimen to shorten treatment. Here, we show that drugs historically classified as sterilizing and non-sterilizing have distinct impacts on a fundamental aspect of Mycobacterium tuberculosis physiology: ribosomal RNA (rRNA) synthesis. In culture, in mice, and in human studies, measurement of precursor rRNA reveals that sterilizing drugs and highly effective drug regimens profoundly suppress M. tuberculosis rRNA synthesis, whereas non-sterilizing drugs and weaker regimens do not. The rRNA synthesis ratio provides a readout of drug effect that is orthogonal to traditional measures of bacterial burden. We propose that this metric of drug activity may accelerate the development of shorter TB regimens.

Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2',2'-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer.

The aim of this work is to build a mechanistic multiscale pharmacokinetic model for the anticancer drug 2',2'-difluorodeoxycytidine (gemcitabine, dFdC), able to describe the concentrations of dFdC metabolites in the pancreatic tumor tissue in dependence of physiological and genetic patient characteristics, and, more in general, to explore the capabilities and limitations of this kind of modeling strategy. A mechanistic model characterizing dFdC metabolic pathway (metabolic network) was developed using in vitro literature data from two pancreatic cancer cell lines. The network was able to describe the time course of extracellular and intracellular dFdC metabolites concentrations. Moreover, a physiologically-based pharmacokinetic model was developed to describe clinical dFdC profiles by using enzymatic and physiological information available in the literature. This model was then coupled with the metabolic network to describe the dFdC active metabolite profile in the pancreatic tumor tissue. Finally, global sensitivity analysis was performed to identify the parameters that mainly drive the interindividual variability for the area under the curve (AUC) of dFdC in plasma and of its active metabolite (dFdCTP) in tumor tissue. From this analysis, cytidine deaminase (CDA) concentration was identified as the main driver of plasma dFdC AUC interindividual variability, whereas CDA and deoxycytidine kinase concentration mainly explained the tumor dFdCTP AUC variability. However, the lack of in vitro and in vivo information needed to characterize key model parameters hampers the development of this kind of mechanistic approach. Further studies to better characterize pancreatic cell lines and patient enzymes polymorphisms are encouraged to refine and validate the current model.

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing.

Hydroxychloroquine (HCQ) is a promising candidate for Coronavirus disease of 2019 (COVID-19) treatment. The optimal dosing of HCQ is unknown. Our goal was to integrate historic and emerging pharmacological and toxicity data to understand safe and efficacious HCQ dosing strategies for COVID-19 treatment. The data sources included were (i) longitudinal clinical, pharmacokinetic (PK), and virologic data from patients with severe acute respiratory syndrome-2 (SARS-CoV-2) infection who received HCQ with or without azithromycin (n = 116), (ii) in vitro viral replication data and SARS-CoV-2 viral load inhibition by HCQ, (iii) a population PK model of HCQ, and (iv) a model relating chloroquine PKs to corrected QT (QTc) prolongation. A mechanistic PK/virologic/QTc model for HCQ was developed and externally validated to predict SARS-CoV-2 rate of viral decline and QTc prolongation. SARS-CoV-2 viral decline was associated with HCQ PKs (P < 0.001). The extrapolated patient half-maximal effective concentration (EC50 ) was 4.7 µM, comparable to the reported in vitro EC50s . HCQ doses > 400 mg b.i.d. for ≥5 days were predicted to rapidly decrease viral loads, reduce the proportion of patients with detectable SARS-CoV-2 infection, and shorten treatment courses, compared with lower dose (≤ 400 mg daily) regimens. However, HCQ doses > 600 mg b.i.d. were also predicted to prolong QTc intervals. This prolongation may have clinical implications warranting further safety assessment. Due to COVID-19's variable natural history, lower dose HCQ regimens may be indistinguishable from controls. Evaluation of higher HCQ doses is needed to ensure adequate safety and efficacy.

Translational Framework Predicting Tumour Response in Gemcitabine-Treated Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft Studies.

The aim of this evaluation was to predict tumour response to gemcitabine in patients with advanced pancreas or ovarian cancer using pre-clinical data obtained from xenograft tumour-bearing mice. The approach consisted of building a translational model combining pre-clinical pharmacokinetic-pharmacodynamic (PKPD) models and parameters, with dosing paradigms used in the clinics along with clinical PK models to derive tumour profiles in humans driving overall survival. Tumour growth inhibition simulations were performed using drug effect parameters obtained from mice, system parameters obtained from mice after appropriate scaling, patient PK models for gemcitabine and carboplatin, and the standard dosing schedules given in the clinical scenario for both types of cancers. Tumour profiles in mice were scaled by body weight to their equivalent values in humans. As models for survival in humans showed that tumour size was the main driver of the hazard rate, it was possible to describe overall survival in pancreatic and ovarian cancer patients. Simulated tumour dynamics in pancreatic and ovarian cancer patients were evaluated using available data from clinical trials. Furthermore, calculated metrics showed values (maximal tumour regression [0-17%] and tumour size ratio at week 12 with respect to baseline [- 9, - 4.5]) in the range of those predicted with the clinical PKPD models. The model-informed Drug Discovery and Development paradigm has been successfully applied retrospectively to gemcitabine data, through a semi-mechanistic translational approach, describing the time course of the tumour response in patients from pre-clinical studies.

Predicting tumour growth and its impact on survival in gemcitabine-treated patients with advanced pancreatic cancer.

The aim of this evaluation was to characterize the impact of the tumour size (TS) effects driven by the anticancer drug gemcitabine on overall survival (OS) in patients with advanced pancreatic cancer by building and validating a predictive semi-mechanistic joint TS-OS model. TS and OS data were obtained from one phase II and one phase III study where gemcitabine was administered (1000-1250 mg/kg over 30-60 min i.v infusion) as single agent to patients (n = 285) with advanced pancreatic cancer. Drug exposure, TS and OS were linked using the population approach with NONMEM 7.3. Pancreatic tumour progression was characterized by exponential growth (doubling time = 67 weeks), and tumour response to treatment was described as a function of the weekly area under the gemcitabine triphosphate concentration vs time curve (AUC), including treatment-related resistance development. The typical predicted percentage of tumour growth inhibition with respect to no treatment was 22.3% at the end of 6 chemotherapy cycles. Emerging resistance elicited a 57% decrease in drug effects during the 6th chemotherapy cycle. Predicted TS profile was identified as main prognostic factor of OS, with tumours responders' profiles improving median OS by 30 weeks compared to stable-disease TS profiles. Results of NCT00574275 trial were predicted using this modelling framework, thereby validating the approach as a prediction tool in clinical development. Our analyses show that despite the advanced stage of the disease in this patient population, the modelling framework herein can be used to predict the likelihood of treatment success using early clinical data.