In silico children and the glass mouse model: clinical trial simulations to identify and individualize optimal isoniazid doses in children with tuberculosis

Pharmacokinetics of First-Line Anti-Tubercular Drugs

Determining the optimal dosages of isoniazid, rifampicin, pyrazinamide and ethambutol in children is necessary to obtain therapeutic serum concentrations of these drugs. Revised dosages have improved the exposure of 1st line anti-tubercular drugs to some extent; there is still scope for modification of the dosages to achieve exposures which can lead to favourable outcome of the disease. High dose of rifampicin is being investigated in clinical trials in adults with some benefit; studies are required in children. Inter-individual pharmacokinetic variability and the effect of age, nutritional status, Human immunodeficiency virus (HIV) infection, acetylator genotype may need to be accounted for in striving for the dosages best suited for an individual.

A programme to create short-course chemotherapy for pulmonary Mycobacterium avium disease based on pharmacokinetics/pharmacodynamics and mathematical forecasting

Objectives

Pulmonary Mycobacterium avium complex (MAC) prevalence is on the rise worldwide. The average therapy duration is 1.5 years, which is associated with poor cure rates. Our objective was to develop a programme to design a combination therapy regimen for pulmonary MAC to be administered for 6 months or less with efficacy in > 90% of patients.

Methods

We performed a literature search for the following MeSH headings 'Mycobacterium avium' AND 'pharmacokinetics/pharmacodynamics' in PubMed up to 2016. The findings were then used to identify steps in the programme to design new regimens with faster microbial kill rates than the current standard regimen.

Results

First, we designed a strategy for rapid in vitro screening of all antibiotic classes for repurposing against pulmonary MAC. Secondly, we identified and compared maximal microbial kill rates (Emax), and optimal exposures of eight different antibiotics. These studies had all been performed in the hollow-fibre system model of pulmonary MAC (HFS-MAC). Thirdly, all drugs with a high Emax at clinically achievable optimal exposures will be chosen, and exposures associated with synergy or additivity for two/three drugs identified based on Bliss independence. Fourthly, the time-kill slopes and resistance suppression of the chosen combinations will be compared with those of standard combination therapy in the HFS-MAC. Finally, we will identify the clinical doses best able to achieve synergistic or additive combination exposures by taking into account pharmacokinetic variability.

Conclusions

Our stepwise pharmacokinetics/pharmacodynamics approach provides a scientific rationale and a strategy for achieving short-course chemotherapy for pulmonary MAC disease within a few years.

A Combination Regimen Design Program Based on Pharmacodynamic Target Setting for Childhood Tuberculosis: Design Rules for the Playground

Children with tuberculosis are treated with drug regimens copied from adults despite significant differences in antibiotic pharmacokinetics, pathology, and the microbial burden between childhood and adult tuberculosis. We sought to develop a new and effective oral treatment regimen specific to children of different ages. We investigated and validated the concept that target drug concentrations associated with therapy failure and death in children are different from those of adults. On that basis, we proposed a 4-step program to rapidly develop treatment regimens for children. First, target drug concentrations for optimal efficacy are derived from preclinical models of disseminated tuberculosis that recapitulate pediatric pharmacokinetics, starting with monotherapy. Second, 2-drug combinations were examined for zones of synergy, antagonism, and additivity based on a whole exposure–response surface. Exposures associated with additivity or synergy were then combined and the regimen was compared to standard therapy. Third, several exposures of the third drug were added, and a 3-drug regimen was identified based on kill slopes in comparison to standard therapy. Fourth, computer-aided clinical trial simulations are used to identify clinical doses that achieve these kill rates in children in different age groups. The proposed program led to the development of a 3-drug combination regimen for children from scratch, independent of adult regimens, in <2 years. The regimens and doses can be tested in animal models and in clinical trials.

Optimal Clinical Doses of Faropenem, Linezolid, and Moxifloxacin in Children With Disseminated Tuberculosis: Goldilocks

Background. When treated with the same antibiotic dose, children achieve different 0- to 24-hour area under the concentration-time curves (AUC_0–24) because of maturation and between-child physiological variability on drug clearance. Children are also infected by Mycobacterium tuberculosis isolates with different antibiotic minimum inhibitory concentrations (MICs). Thus, each child will achieve different AUC_0–24/MIC ratios when treated with the same dose.

Methods. We used 10 000-subject Monte Carlo experiments to identify the oral doses of linezolid, moxifloxacin, and faropenem that would achieve optimal target exposures associated with optimal efficacy in children with disseminated tuberculosis. The linezolid and moxifloxacin exposure targets were AUC_0–24/MIC ratios of 62 and 122, and a faropenem percentage of time above MIC >60%, in combination therapy. A linezolid AUC_0–24 of 93.4 mg × hour/L was target for toxicity. Population pharmacokinetic parameters of each drug and between-child variability, as well as MIC distribution, were used, and the cumulative fraction of response (CFR) was calculated. We also considered drug penetration indices into meninges, bone, and peritoneum.

Results. The linezolid dose of 15 mg/kg in full-term neonates and infants aged up to 3 months and 10 mg/kg in toddlers, administered once daily, achieved CFR ≥ 90%, with <10% achieving linezolid AUC_0–24 associated with toxicity. The moxifloxacin dose of 25 mg/kg/day achieved a CFR > 90% in infants, but the optimal dose was 20 mg/kg/day in older children. The faropenem medoxomil optimal dosage was 30 mg/kg 3–4 times daily.

Conclusions. The regimen and doses of linezolid, moxifloxacin, and faropenem identified are proposed to be adequate for all disseminated tuberculosis syndromes, whether drug-resistant or -susceptible.

Drug Concentration Thresholds Predictive of Therapy Failure and Death in Children With Tuberculosis: Bread Crumb Trails in Random Forests

Background. The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown.

Methods. Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables.

Results. Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28–5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08–4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99–11.82).

Conclusions. We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis.

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

Global tuberculosis incidence has declined marginally over the past decade, and tuberculosis remains out of control in several parts of the world including Africa and Asia. Although tuberculosis control has been effective in some regions of the world, these gains are threatened by the increasing burden of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis. XDR tuberculosis has evolved in several tuberculosis-endemic countries to drug-incurable or programmatically incurable tuberculosis (totally drug-resistant tuberculosis). This poses several challenges similar to those encountered in the pre-chemotherapy era, including the inability to cure tuberculosis, high mortality, and the need for alternative methods to prevent disease transmission. This phenomenon mirrors the worldwide increase in antimicrobial resistance and the emergence of other MDR pathogens, such as malaria, HIV, and Gram-negative bacteria. MDR and XDR tuberculosis are associated with high morbidity and substantial mortality, are a threat to health-care workers, prohibitively expensive to treat, and are therefore a serious public health problem. In this Commission, we examine several aspects of drug-resistant tuberculosis. The traditional view that acquired resistance to antituberculous drugs is driven by poor compliance and programmatic failure is now being questioned, and several lines of evidence suggest that alternative mechanisms-including pharmacokinetic variability, induction of efflux pumps that transport the drug out of cells, and suboptimal drug penetration into tuberculosis lesions-are likely crucial to the pathogenesis of drug-resistant tuberculosis. These factors have implications for the design of new interventions, drug delivery and dosing mechanisms, and public health policy. We discuss epidemiology and transmission dynamics, including new insights into the fundamental biology of transmission, and we review the utility of newer diagnostic tools, including molecular tests and next-generation whole-genome sequencing, and their potential for clinical effectiveness. Relevant research priorities are highlighted, including optimal medical and surgical management, the role of newer and repurposed drugs (including bedaquiline, delamanid, and linezolid), pharmacokinetic and pharmacodynamic considerations, preventive strategies (such as prophylaxis in MDR and XDR contacts), palliative and patient-orientated care aspects, and medicolegal and ethical issues.

The Non-Linear Child: Ontogeny, Isoniazid Concentration, and NAT2 Genotype Modulate Enzyme Reaction Kinetics and Metabolism

N-acetyltransferase 2 (NAT2) catalyzes the acetylation of isoniazid to N-acetylisoniazid. NAT2 polymorphism explains 88% of isoniazid clearance variability in adults. We examined the effects of clinical and genetic factors on Michaelis-Menten reaction kinetic constants of maximum velocity (V_max) and affinity (K_m) in children 0–10 years old. We measured the rates of isoniazid elimination and N-acetylisoniazid production in the blood of 30 children. Since maturation effects could be non-linear, we utilized a pharmacometric approach and the artificial intelligence method, multivariate adaptive regression splines (MARS), to identify factors predicting NAT2 V_max and K_m by examining clinical, genetic, and laboratory factors in toto. Isoniazid concentration predicted both V_max and K_m and superseded the contribution of NAT2 genotype. Age non-linearly modified the NAT2 genotype contribution until maturation at ≥ 5.3 years. Thus, enzyme efficiency was constrained by substrate concentration, genes, and age. Since MARS output is in the form of basis functions and equations, it allows multiscale systems modeling from the level of cellular chemical reactions to whole body physiological parameters, by automatic selection of significant predictors by the algorithm.

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We identified the NAT2 K_m and V_max in children treated with isoniazid.

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Artificial intelligence (AI) algorithms were used to find predictors of K_m and V_max.

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Isoniazid concentration affected V_max and K_m, and superseded NAT2 genotype.

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Age non-linearly modified NAT2 genotype contribution until maturation at ≥ 5.3 years.

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AI output is in the form of equations that allow multiscale systems modeling.

The effects of maturation on drug metabolism have not been studied for the type phase II enzymes such as NAT2, which metabolizes the drug isoniazid. Genes have been found to control speed of isoniazid metabolism. Studies to characterize affinity and maximum velocity for isoniazid metabolism in people were last performed in two individuals' livers in the 1960s. We identified NAT2 affinity and maximum velocity in 30 tuberculosis children treated with isoniazid. Artificial intelligence methods found that metabolism was affected by the drug's concentration more than by genes, which were affected by age up to 5.3 years.

A multi-scale approach to designing therapeutics for tuberculosis

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

Design of a drug-drug interaction study of vincristine with azole antifungals in pediatric cancer patients using clinical trial simulation

BACKGROUND

The aim of the current work was to perform a clinical trial simulation (CTS) analysis to optimize a drug-drug interaction (DDI) study of vincristine in children who also received azole antifungals, taking into account challenges of conducting clinical trials in this population, and, to provide a motivating example of the application of CTS in the design of pediatric oncology clinical trials.

PROCEDURE

A pharmacokinetic (PK) model for vincristine in children was used to simulate concentration-time profiles. A continuous model for body surface area versus age was defined based on pediatric growth curves. Informative sampling time windows were derived using D-optimal design. The CTS framework was used to different magnitudes of clearance inhibition (10%, 25%, or 40%), sample size (30-500), the impact of missing samples or sampling occasions, and the age distribution, on the power to detect a significant inhibition effect, and in addition, the relative estimation error (REE) of the interaction effect.

RESULTS

A minimum group specific sample size of 38 patients with a total sample size of 150 patients was required to detect a clearance inhibition effect of 40% with 80% power, while in the case of a lower effect of clearance inhibition, a substantially larger sample size was required. However, for the majority of re-estimated drug effects, the inhibition effect could be estimated precisely (REE < 25%) in even smaller sample sizes and with lower effect sizes.

CONCLUSION

This work demonstrated the utility of CTS for the evaluation of PK clinical trial designs in the pediatric oncology population.

Therapy duration and long-term outcomes in extra-pulmonary tuberculosis

BACKGROUND

Tuberculosis is classified as either pulmonary or extra-pulmonary (EPTB). While much focus has been paid to pulmonary tuberculosis, EPTB has received scant attention. Moreover, EPTB is viewed as one wastebasket diagnosis, as "the other" which is not pulmonary.

METHODS

This is a retrospective cohort study of all patients treated for EPTB in the state of Texas between January 2000 and December 2005, who had no pulmonary disease. Clinical and epidemiological factors were abstracted from electronic records of the Report of Verified Case of Tuberculosis. The long-term outcome, which is death by December 2011, was established using the Social Security Administration Death Master File database. Survival in EPTB patients was compared to those with latent tuberculosis, as well as between different types of EPTB, using Cox proportional hazard models. A hybrid of the machine learning method of classification and regression tree analyses and standard regression models was used to identify high-order interactions and clinical factors predictive of long-term all-cause mortality.

RESULTS

Four hundred and thirty eight patients met study criteria; the median study follow-up period for the cohort was 7.8 (inter-quartile range 6.0-10.1) years. The overall all-cause mortality rate was 0.025 (95% confidence interval [CI]: 0.021-0.030) per 100 person-year of follow-up. The significant predictors of poor long-term outcome were age (hazard ratio [HR] for each year of age-at-diagnosis was 1.05 [CI: 1.04-1.06], treatment duration, type of EPTB and HIV-infection (HR = 2.16; CI: 1.22, 3.83). Mortality in genitourinary tuberculosis was no different from latent tuberculosis, while meningitis had the poorest long-term outcome of 46.2%. Compared to meningitis the HR for death was 0.50 (CI: 0.27-0.91) for lymphatic disease, 0.42 (CI: 0.21-0.81) for bone/joint disease, and 0.59 (CI: 0.27-1.31) for peritonitis. The relationship between mortality and therapy duration for each type of EPTB was a unique "V" shaped curve, with the lowest mortality observed at different therapy durations for each, beyond which mortality increased.

CONCLUSIONS

EPTB is comprised of several different diseases with different outcomes and durations of therapy. The "V" shaped relationship between therapy duration and outcome leads to the hypothesis that longer duration of therapy may lead to higher patient mortality.

Pharmacogenomics and Personalized Medicine for Infectious Diseases

Humans have been plagued by the scourge of invasion by pathogens leading to infectious diseases from the time in memoriam and are still the cause of morbidity and mortality among millions of individuals. Trying to understand the disease mechanisms and finding the remedial measures have been the quest of humankind. The susceptibility to disease of an individual in a given population is determined by ones genetic buildup. Response to treatment and the disease prognosis also depends upon individual’s genetic predisposition. The environmental stress induces mutations and is leading to the emergence of ever-increasing more dreaded infectious pathogens, and now we are in the era of increasing antibiotic resistance that has thrown up a challenge to find new treatment regimes. Discoveries in the science of high-throughput sequencing and array technologies have shown new hope and are bringing a revolution in human health. The information gained from sequencing of both human and pathogen genomes is a way forward in deciphering host-pathogen interactions. Deciphering the pathogen virulence factors, host susceptibility genes, and the molecular programs involved in the pathogenesis of disease has paved the way for discovery of new molecular targets for drugs, diagnostic markers, and vaccines. The genomic diversity in the human population leads to differences in host responses to drugs and vaccines and is the cause of poor response to treatment as well as adverse reactions. The study of pharmacogenomics of infectious diseases is still at an early stage of development, and many intricacies of the host-pathogen interaction are yet to be understood in full measure. However, progress has been made over the decades of research in some of the important infectious diseases revealing how the host genetic polymorphisms of drug-metabolizing enzymes and transporters affect the bioavailability of the drugs which further determine the efficacy and toxicology of the drugs used for treatment. Further, the field of structural biology and chemistry has intertwined to give rise to medical structural genomics leading the way to the discovery of new drug targets against infectious diseases. This chapter explores how the advent of “omics” technologies is making a beginning in bringing about a change in the prevention, diagnosis, and treatments of the infectious diseases and hence paving way for personalized medicine.

Role of pharmacogenomics in the treatment of tuberculosis: a review

BACKGROUND

Tuberculosis is one of the major public health problems worldwide. Modern antituberculous treatment can cure most patients; cure rates > 95% are achieved with standard short-course chemotherapy regimens containing isoniazid, rifampicin, pyrazinamide, and ethambutol among patients with drug-susceptible strains of tuberculosis; however, a small proportion do not respond to treatment or develop serious adverse events. Pharmacogenomic studies of drugs used in the treatment of tuberculosis could help us understand intersubject variations in treatment response. In this review, we compiled pharmacogenomic data on antituberculous drugs that were available from different settings that would give a better insight into the role of pharmacogenomics in the treatment of tuberculosis, thereby enhancing the efficacy and limiting the toxicity of existing antituberculosis medications.

METHODS

The PubMed database was searched from 1960 to the present using the keywords "tuberculosis", "antituberculosis treatment", "isoniazid", "rifampicin", "pyrazinamide", "ethambutol", "pharmacogenomics", and "polymorphism". Abstracts from meetings and review articles were included.

CONCLUSION

Studies conducted in different settings suggest that pharmacogenomics plays a significant role in isoniazid metabolism, and impacts both treatment efficacy and frequency of adverse reactions. Single nucleotide polymorphisms influencing plasma rifampicin concentrations are also reported. No data are available regarding other first-line drugs, ie, ethambutol and pyrazinamide. There is a need to incorporate pharmacogenomics into clinical trials of tuberculosis in order to understand the factors impacting therapeutic success and occurrence of adverse drug effects.

Pharmacokinetic evaluation of the penetration of antituberculosis agents in rabbit pulmonary lesions

Standard antituberculosis (anti-TB) therapy requires the use of multiple drugs for a minimum of 6 months, with variable outcomes that are influenced by a number of microbiological, pathological, and clinical factors. This is despite the availability of antibiotics that have good activity against Mycobacterium tuberculosis in vitro and favorable pharmacokinetic profiles in plasma. However, little is known about the distribution of widely used antituberculous agents in the pulmonary lesions where the pathogen resides. The rabbit model of TB infection was used to explore the hypothesis that standard drugs have various abilities to penetrate lung tissue and lesions and that adequate drug levels are not consistently reached at the site of infection. Using noncompartmental and population pharmacokinetic approaches, we modeled the rate and extent of distribution of isoniazid, rifampin, pyrazinamide, and moxifloxacin in rabbit lung and lesions. Moxifloxacin reproducibly showed favorable partitioning into lung and granulomas, while the exposure of isoniazid, rifampin, and pyrazinamide in lesions was markedly lower than in plasma. The extent of penetration in lung and lesions followed different trends for each drug. All four agents distributed rapidly from plasma to tissue with equilibration half-lives of less than 1 min to an hour. The models adequately described the plasma concentrations and reasonably captured actual lesion concentrations. Though further refinement is needed to accurately predict the behavior of these drugs in human subjects, our results enable the integration of lesion-specific pharmacokinetic-pharmacodynamic (PK-PD) indices in clinical trial simulations and in in vitro PK-PD studies with M. tuberculosis.