Interchangeability of generic drugs for subpopulations: Bioequivalence simulation from a nonparametric PK model of gabapentin generic drugs.

Patients are often switched between generic formulations of the same drug, but in some cases generic interchangeability is questioned. For generic drugs to be approved, bioequivalence with the innovator drug should be demonstrated, but evidence of bioequivalence is not required in the intended patient population or relative to other approved generics. AIM: We aim to identify pathophysiological pharmacokinetic subpopulations for whom there is a difference in comparative bioavailability compared to a healthy population. METHODS: We used simulated exposures from a nonparametric model of multiple generics and the originator gabapentin. Exposure was simulated for virtual populations with pharmacokinetic characteristics beyond those of healthy subjects with regard to rate of absorption, volume of distribution and reduced renal function. Virtual parallel design bioequivalence studies were performed using a random sample of 24 simulated subjects, with standard acceptance criteria. RESULTS: Results indicated increased pharmacokinetic variability for patient populations with a lower rate of absorption or a reduced renal function, but no change in the average comparable bioavailability ratio. This increased variability results in a reduced likelihood of demonstrating bioequivalence. Observations were similar for comparisons between all different formulations, as well as between subjects who received the identical formulation in a repeated fashion. No relevant effect was observed for simulations with increased volume of distribution. CONCLUSION: Our simulations indicate that the reduced likelihood of demonstrating bioequivalence for subjects with altered pharmacokinetics is not influenced by a formulation switch, nor does the average comparable bioavailability ratio change, therefore these results support generic interchangeability and current approval requirements for generics.

Parametric and Nonparametric Population Pharmacokinetic Models to Assess Probability of Target Attainment of Imipenem Concentrations in Critically Ill Patients.

Population pharmacokinetic modeling and simulation (M&S) are used to improve antibiotic dosing. Little is known about the differences in parametric and nonparametric M&S. Our objectives were to compare (1) the external validation of parametric and nonparametric models of imipenem in critically ill patients and (2) the probability of target attainment (PTA) calculations using simulations of both models. The M&S software used was NONMEM 7.2 (parametric) and Pmetrics 1.5.2 (nonparametric). The external predictive performance of both models was adequate for eGFRs ≥ 78 mL/min but insufficient for lower eGFRs, indicating that the models (developed using a population with eGFR ≥ 60 mL/min) could not be extrapolated to lower eGFRs. Simulations were performed for three dosing regimens and three eGFRs (90, 120, 150 mL/min). Fifty percent of the PTA results were similar for both models, while for the other 50% the nonparametric model resulted in lower MICs. This was explained by a higher estimated between-subject variability of the nonparametric model. Simulations indicated that 1000 mg q6h is suitable to reach MICs of 2 mg/L for eGFRs of 90-120 mL/min. For MICs of 4 mg/L and for higher eGFRs, dosing recommendations are missing due to largely different PTA values per model. The consequences of the different modeling approaches in clinical practice should be further investigated.

Building Optimal Three-Drug Combination Chemotherapy Regimens To Eradicate Mycobacterium tuberculosis in Its Slow-Growth Acid Phase.

Mycobacterium tuberculosis metabolic state affects the response to therapy. Quantifying the effect of antimicrobials in the acid and nonreplicating metabolic phases of M. tuberculosis growth will help to optimize therapy for tuberculosis. As a brute-force approach to all possible drug combinations against M. tuberculosis in all different metabolic states is impossible, we have adopted a model-informed strategy to accelerate the discovery. Using multiple concentrations of each drug in time-kill studies, we examined single drugs and two- and three-drug combinations of pretomanid, moxifloxacin, and bedaquiline plus its active metabolite against M. tuberculosis in its acid-phase metabolic state. We used a nonparametric modeling approach to generate full distributions of interaction terms between pretomanid and moxifloxacin for susceptible and less susceptible populations. From the model, we could predict the 95% confidence interval of the simulated total bacterial population decline due to the 2-drug combination regimen of pretomanid and moxifloxacin and compare this to observed declines with 3-drug regimens. We found that the combination of pretomanid and moxifloxacin at concentrations equivalent to average or peak human concentrations effectively eradicated M. tuberculosis in its acid growth phase and prevented emergence of less susceptible isolates. The addition of bedaquiline as a third drug shortened time to total and less susceptible bacterial suppression by 8 days compared to the 2-drug regimen, which was significantly faster than the 2-drug kill.

Building Optimal Three-Drug Combination Chemotherapy Regimens.

Multidrug therapy is often required. Examples include antiviral therapy, nosocomial infections, and, most commonly, anti-Mycobacterium tuberculosis therapy. Our laboratory previously identified a mathematical approach to identify 2-drug regimens with a synergistic or additive interaction using a full factorial study design. Our objective here was to generate a method to identify an optimal 3-drug therapy. We studied M. tuberculosis isolate H37Rv in log-phase growth in flasks. Pretomanid and moxifloxacin were chosen as the base 2-drug regimen. Bedaquiline (plus M2 metabolite) was chosen as the third drug for evaluation. Total bacterial burden and bacterial burden less-susceptible to study drugs were enumerated. A large mathematical model was fit to all the data. This allowed extension to evaluation of the 3-drug regimen by employing a Monte Carlo simulation. Pretomanid plus moxifloxacin demonstrated excellent bacterial kill and suppressed amplification of less-susceptible pathogens. Total bacterial burden was driven to extinction in 3 weeks in 6 of 9 combination therapy evaluations. Only the lowest pretomanid/moxifloxacin exposures in combination did not extinguish the bacterial burden. No combination regimen allowed resistance amplification. Generation of 95% credible intervals about estimates of the interaction parameters α (αs, αr-p, and αr-m) by bootstrapping showed the interaction was near synergistic. The addition of bedaquiline/M2 metabolite was evaluated by forming a 95% confidence interval regarding the decline in bacterial burden. The addition of bedaquiline/M2 metabolite shortened the time to eradication by 1 week and was significantly different. A model-based system approach to evaluating combinations of 3 agents shows promise to rapidly identify the most promising combinations that can then be trialed.

Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate.

BACKGROUND: Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results. METHODS: Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m2 (interquartile range 104-124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software. RESULTS: For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (Ke). The parametric population parameter estimates were Ke 0.637 h-1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (Vc) 29.6 L (without BSV). The nonparametric values were Ke 0.681 h-1 (34.0% CV) and Vc 31.1 L (42.6% CV). CONCLUSIONS: Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics.

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Interchangeability of Generic Drugs: A Nonparametric Pharmacokinetic Model of Gabapentin Generic Drugs.

Substitution by generic drugs is allowed when bioequivalence to the originator drug has been established. However, it is known that similarity in exposure may not be achieved at every occasion for all individual patients when switching between formulations. The ultimate aim of our research is to investigate if pharmacokinetic subpopulations exist when subjects are exposed to bioequivalent formulations. For that purpose, we developed a pharmacokinetic model for gabapentin, based on data from a previously conducted bioavailability study comparing gabapentin exposure following administration of the gabapentin originator and three generic gabapentin formulations in healthy subjects. Both internal and external validation confirmed that the optimal model for description of the gabapentin pharmacokinetics in this comparative bioavailability study was a two-compartment model with absorption constant, an absorption lag time, and clearance adjusted for renal function, in which each model parameter was separately estimated per administered formulation.

Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R.

INTRODUCTION: Nonparametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic subgroups and outliers within a study population. METHODS: The authors created "Pmetrics," a new Windows and Unix R software package that updates the older MM-USCPACK software for nonparametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative 2-stage Bayesian and the nonparametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (Kel) and unimodal volume of distribution (Vd), plus an extreme outlier, for a 1-compartment model of an intravenous drug. RESULTS: The true means (SD) for Kel and Vd in the population sample were 0.19 (0.17) and 102 (22.3), respectively. Those found by NPAG were 0.19 (0.16) and 104 (22.6). The iterative 2-stage Bayesian estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of Kel, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for Kel and to find the outlier in both the population model and in the Bayesian posterior parameter estimates. CONCLUSIONS: Built on over 3 decades of work, Pmetrics adopts a robust, reliable, and mature nonparametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic subgroups and an outlier.

Nonlinear dynamic neural network for text-independent speaker identification using information theoretic learning technology.

In this paper we present a novel design for a nonlinear dynamic neural network to implement text-independent speaker recognition without the benefit of exact voice signatures. The dynamic properties between the input neuron and the output neuron make use of a nonlinear high-order synaptic neural model with memory of previous input signals. The dynamic neural network is realized in the short-term-frequency long-term-temporal domain. Informatics metric is used to overcome the challenge of performing blind learning for the nonlinear network. The goal of this study is not only to improve the recognition performance but also to amplify the distinctiveness among different speakers.

Tuning and timing in the gerbil ear: Wiener-kernel analysis.

Information about the tuning and timing of excitation in cochlear axons with low-characteristic frequency (CF) is embodied in the first-order Wiener kernel, or reverse correlation function. For high-CF axons, the highest-ranking eigenvector (or singular vector) of the second-order Wiener kernel often can serve as a surrogate for the first-order kernel, providing the same information. For mid-CF axons, the two functions are essentially identical. In this paper we apply these tools to gerbil cochlear-nerve axons with CFs ranging from 700 Hz to 14 kHz. Eigen or singular-value decomposition of the second-order Wiener kernel allows us to separate excitatory and suppressive effects, and to determine precisely the timing of the latter.

Tuning and timing of excitation and inhibition in primary auditory nerve fibers.

Information about the tuning and timing of excitation, adaptation and suppression in an auditory primary afferent axon can be obtained from the second-order Wiener kernel. Through the process of singular-value decomposition, this information can be extracted from the kernel and displayed graphically in separate two-dimensional images for excitation and inhibition(1). For low- to mid-frequency units, the images typically include checkerboard patterns. For all units they may include patterns of parallel diagonal lines. The former represent non-linearities in the phase-locked (ac) response of the unit; the latter reflect non-linear envelope-following (dc) responses. Examples of detailed interpretation are presented for three amphibian-papillar units from the American bullfrog. The second-order Wiener kernel itself is derived from second-order reverse correlation between spikes and a continuous, non-repeating, broad-band white-noise stimulus.