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Many pharmacologically active molecules are formulated as solid dosage form drug products. Following oral administration, the diffusion of an active molecule from the gastrointestinal tract into systemic distribution requires the disintegration of the dosage form followed by the dissolution of the molecule in the stomach lumen. Its dissolution properties may have a direct impact on its bioavailability and subsequent therapeutic effect. Consequently, dissolution (or in vitro release) testing has been the subject of intense scientific and regulatory interest over the past several decades. Much interest has focused on models describing in vitro release profiles over a time scale, and a number of methods have been proposed for testing similarity of profiles. In this article, we review previously published work on dissolution profile similarity testing and provide a detailed critique of current methods in order to set the stage for a Bayesian approach.

GSK1322322 is a novel inhibitor of peptide deformylase (PDF) with good in vitro activity against bacteria associated with community-acquired pneumonia and skin infections. We have characterized the in vivo pharmacodynamics (PD) of GSK1322322 in immunocompetent animal models of infection with Streptococcus pneumoniae and Haemophilus influenzae (mouse lung model) and with Staphylococcus aureus (rat abscess model) and determined the pharmacokinetic (PK)/PD index that best correlates with efficacy and its magnitude. Oral PK studies with both models showed slightly higher-than-dose-proportional exposure, with 3-fold increases in area under the concentration-time curve (AUC) with doubling doses. GSK1322322 exhibited dose-dependent in vivo efficacy against multiple isolates of S. pneumonia e, H. influenzae , and S. aureus . Dose fractionation studies with two S. pneumoniae and S. aureus isolates showed that therapeutic outcome correlated best with the free AUC/MIC ( f AUC/MIC) index in S. pneumoniae ( R 2 , 0.83), whereas f AUC/MIC and free maximum drug concentration ( fC max )/MIC were the best efficacy predictors for S. aureus ( R 2 , 0.9 and 0.91, respectively). Median daily f AUC/MIC values required for stasis and for a 1-log 10 reduction in bacterial burden were 8.1 and 14.4 for 11 S. pneumoniae isolates ( R 2 , 0.62) and 7.2 and 13.0 for five H. influenzae isolates ( R 2 , 0.93). The data showed that for eight S. aureus isolates, f AUC correlated better with efficacy than f AUC/MIC ( R 2 , 0.91 and 0.76, respectively), as efficacious AUCs were similar for all isolates, independent of their GSK1322322 MIC (range, 0.5 to 4 μg/ml). Median f AUCs of 2.1 and 6.3 μg · h/ml were associated with stasis and 1-log 10 reductions, respectively, for S. aureus .

Under the Loewe additivity, constant relative potency between two drugs is a sufficient condition for the two drugs to be additive. Implicit in this condition is that one drug acts like a dilution of the other. Geometrically, it means that the dose-response curve of one drug is a copy of another that is shifted horizontally by a constant over the log-dose axis. Such phenomenon is often referred to as parallelism. Thus, testing drug additivity is equivalent to the demonstration of parallelism between two dose-response curves. Current methods used for testing parallelism are usually based on significance tests for differences between parameters in the dose-response curves of the monotherapies. A p-value of less than 0.05 is indicative of non-parallelism. The p-value-based methods, however, may be fundamentally flawed because an increase in either sample size or precision of the assay used to measure drug effect may result in more frequent rejection of parallel lines for a trivial difference. Moreover, similarity (difference) between model parameters does not necessarily translate into the similarity (difference) between the two response curves. As a result, a test may conclude that the model parameters are similar (different), yet there is little assurance on the similarity between the two dose-response curves. In this paper, we introduce a Bayesian approach to directly test the hypothesis that the two drugs have a constant relative potency. An important utility of our proposed method is in aiding go/no-go decisions concerning two drug combination studies. It is illustrated with both a simulated example and a real-life example. Copyright © 2015 John Wiley & Sons, Ltd.

The prior distribution reflects knowledge and uncertainty of the modeled parameters. Determining the prior distribution for a dose-finding clinical trial can be influential in its design and analysis. Using the planning of a phase 2 trial for COPD with a dose-response curve as a case study, we illustrate the use of relevant historical data for the nonlinear curve mean-model parameters as well as consideration for terms to characterize between-trial and within-trial variability. Through a predictive inference exercise, a data-driven informative prior distribution is constructed for the future study. We share our strategies on how to obtain informative Bayesian priors for both design and analysis of dose-finding clinical trials using relevant historical data and deal with the associated issues.

Numerous investigations have linked mitochondrial dysfunction to adverse health outcomes and drug-induced toxicity. The pharmaceutical industry is challenged with identifying mitochondrial liabilities earlier in drug development and thereby reducing late-stage attrition. Consequently, there is a demand for reliable, higher-throughput screening methods for assessing the impact of drug candidates on mitochondrial function. The extracellular flux (XF) assay described here is a plate-based method in which galactose-conditioned HepG2 cells were acutely exposed to test compounds, then real-time changes in the oxygen consumption rate and extracellular acidification rate were simultaneously measured using a Seahorse Bioscience XF-96 analyzer. The acute XF assay was validated using marketed drugs known to modulate mitochondrial function, and data analysis was automated using a spline curve fitting model developed at GlaxoSmithKline. We demonstrate that the acute XF assay is a robust, sensitive screening platform for evaluating drug-induced effects on mitochondrial activity in whole cells.

Validation of linearity is a regulatory requirement. Although many methods are proposed, they suffer from several deficiencies including difficulties of setting fit-for-purpose acceptable limits, dependency on concentration levels used in linearity experiment, and challenges in implementation for statistically lay users. In this article, a statistical procedure for testing linearity is proposed. The method uses a two one-sided test (TOST) of equivalence to evaluate the bias that can result from approximating a higher-order polynomial response with a linear function. By using orthogonal polynomials and generalized pivotal quantity analysis, the method provides a closed-form solution, thus making linearity testing easy to implement.

Many modern compound-screening technologies are highly miniaturized, resulting in longer-lasting solution stocks in compound management laboratories. As the ages of some stocks stretch into years, it becomes increasingly important to ensure that the DMSO solutions remain of high quality. It can be a burden to check the quality of a large library of compound solutions continuously, and so a study was devised to link the effects of initial compound purity and physicochemical properties of the compounds with the current purity of DMSO solutions. Approximately 5000 compounds with initial purity of at least 80% were examined. Storage conditions were held or observed to be relatively constant and so were eliminated as potential predictors. This allowed the evaluation of the effects of other factors on the stability of solutions, such as initial purity, number of freeze-thaw cycles, age of the solution, and multiple calculated physicochemical parameters. Of all the factors investigated, initial purity was the only one that had a clear effect on stability. None of the other parameters investigated (physicochemical properties, number of freeze-thaw cycles, age of solutions) had a statistically significant effect on stability.

The ICH Q2(R1) (International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use) guideline for testing linearity in validation of analytical procedures suggests that “linearity should be evaluated by visual inspection of a plot of signals as a function of analyte concentration or content.” The EP6-A guideline recommends more quantitative methods that compare straight-line and higher-order polynomial curve fits. In this paper, a new equivalence test is proposed to compare the quality of a straight-line fit to that of a higher-order polynomial. By using orthogonal polynomials and generalized pivotal quantity analysis, one may estimate the probability of equivalence between a straight line and a polynomial curve fit either in the assay signal space (the Y values) or in the concentration space (the X values). In the special case of the linear-to-quadratic polynomial comparison, an equivalence test may be constructed via a two one-sided T test. Copyright © 2013 John Wiley & Sons, Ltd.

Early drug discovery laboratories often call for the precise weighing of 1- to 5-mg solids into 4- to 5-g glass vials. For the balance used in this study (Mettler Toledo XP205), the manufacturer rates its accuracy at ±0.01 mg over the working range of 1 mg to 220 g and its precision or repeatability at 0.015 mg for 10-g weights. The manufacturer ratings were confirmed using standard steel weights, but these calibrators do not well represent the weighing precision of drug compound. For example, when pre-taring a 4- to 5-g vial on the balance and then weighing 1- to 5-mg calibration weights, although no bias was observed, precision dropped appreciably. When measuring solid sample in the range of 1 to 5 mg, deviation of the measured weight from the actual (true) weight was even worse, in the range of ±20% to 50%. Balance settings and environmental factors exert a strong influence on weighing precision. Although most environmental factors, such as air draughts, temperature, vibrations, and levelness, can be optimized to the extent practical in laboratory settings, problems due to static electricity are often overlooked. By controlling static electricity, we demonstrate how we optimized the process to where measurements were within ±10% of actual weight when weighing solid sample in the range of 2 to 5 mg and ±20% when weighing 1 mg into a 4- to 5-g vial. Our weighing process and method to calculate actual weight are given in detail.

Drug-induced prolongation of the QT interval via block of the hERG potassium channel is a major cause of attrition in drug development. The advent of automated electrophysiology systems has enabled the detection of hERG block earlier in drug discovery. In this study, we have evaluated the suitability of a second generation automated patch clamp instrument, the IonWorks Barracuda, for the characterization of hERG biophysics and pharmacology.All experiments were conducted with cells stably expressing hERG. Recordings were made in perforated patch mode either on a conventional patch clamp setup or on the IonWorks Barracuda. On the latter, all recordings were population recordings in 384-well patch plates.HERG channels activated with a V(1/2)=-3.2±1.6mV (n=178) on the IonWorks Barracuda versus -11.2±6.1mV (n=9) by manual patch clamp. On the IonWorks Barracuda, seal resistances and currents were stable (30% change) with up to six cumulative drug additions and 1-min incubations per addition. Over 27 experiments, an average of 338 concentration-response curves were obtained per experiment (96% of the 352 test wells on each plate). HERG pharmacology was examined with a set of 353 compounds that included well-characterized hERG blockers. Astemizole, terfenadine and quinidine inhibited hERG currents with IC(50) values of 159nM, 224nM and 2μM, respectively (n=51, 10 and 18). This set of compounds was also tested on the PatchXpress automated electrophysiology system. We determined through statistical methods that the two automated systems provided equivalent results.Evaluating drug effects on hERG channels is best performed by electrophysiological methods. HERG activation and pharmacology on the IonWorks Barracuda automated electrophysiology platform were in good agreement with published electrophysiology results. Therefore, the IonWorks Barracuda provides an efficient way to study hERG biophysics and pharmacology.

Parallelism is a prerequisite for the determination of relative potency in bioassays. It involves testing of similarity between a pair of dose–response curves of a reference standard and a test sample. Methods for parallelism assessment that are currently in use include p-value-based significance tests and interval-based equivalence tests. These methods make statistical inference about the similarity between the model parameters of the dose–response curves based on the sampling distribution of the estimates of these parameters. Although the methods have some merits for parallelism testing, there is a major drawback to these approaches, namely that the similarity between the model parameters does not necessarily translate into the similarity between the two dose–response curves. As a result, a test may conclude that the model parameters are similar, yet there is little assurance on the similarity between the two dose–response curves. In this article, we reformulate the parallelism testing problem as testin...

Drug discovery scientists routinely develop and use in-vitro assays; for example, to identify “hits,” or to quantify the efficacious concentrations of compounds in a lead series. New and improved assays are developed to replace existing ones as the new assays may be cheaper, faster, or easier to use. An existing assay typically cannot be replaced until the new format is determined to produce equivalent measurements to the original on a test set of compounds with a diverse range of activity. In this article we propose two definitions for assessing assay equivalence across a range of responses, and apply Bayesian methods to estimate the probability of assay equivalence. Data are modeled via orthogonal regression for the case where the relative variability of the two assays is unknown a priori, and replicate measurements for each assay and compound are sufficient to identify the full set of model parameters in a likelihood model. The article reports results of a simulation experiment to explore the performan...

Activation of seven-transmembrane (7TM) receptors by agonists does not always lead to uniform activation of all signaling pathways mediated by a given receptor. Relative to other ligands, many agonists are "biased" toward producing subsets of receptor behaviors. A hallmark of such "functional selectivity" is cell type dependence; this poses a particular problem for the profiling of agonists in whole cell test systems removed from the therapeutic one(s). Such response-specific cell-based variability makes it difficult to guide medicinal chemistry efforts aimed at identifying and optimizing therapeutically meaningful agonist bias. For this reason, we present a scale, based on the Black and Leff operational model, that contains the key elements required to describe 7TM agonism, namely, affinity (K(A) (-1)) for the receptor and efficacy (τ) in activating a particular signaling pathway. Utilizing a "transduction coefficient" term, log(τ/K(A)), this scale can statistically evaluate selective agonist effects in a manner that can theoretically inform structure-activity studies and/or drug candidate selection matrices. The bias of four chemokines for CCR5-mediated inositol phosphate production versus internalization is quantified to illustrate the practical application of this method. The independence of this method with respect to receptor density and the calculation of statistical estimates of confidence of differences are specifically discussed.

The Food and Drug Administration (FDA) has proposed a parametric tolerance interval test (PTIT) for batch-release testing of inhalation devices. The proposed test examines dose uniformity based on several inhalation units from a batch, with two observations per unit. An underlying assumption is that the observations are a random sample from a univariate normal distribution. Because there are two observations per unit, it may be more appropriate to model the data as stemming from a bivariate normal distribution. We take a bivariate approach and use generalized confidence interval methodology to derive a parametric tolerance interval for the distribution of doses within a batch. We then use Monte Carlo simulation to compare results based on this bivariate approach with those based on the FDA-proposed PTIT.

Human diseases may involve cellular signaling networks that contain redundant pathways, so that blocking a single pathway in the system cannot achieve the desired effect. As such, the use of drugs in combination are particularly effective interventions in networked systems. However, common synergy measures are often inadequate to quantify the effect of two different drugs in complex cellular systems. This article proposes a general approach to quantifying the synergy of two drugs in combination. This approach is called strong nonlinear blending. Drugs with different relative potencies, different effect maxima, or situations of potentiation or coalism pose no problem for strong nonlinear blending as a way to assess the increased response benefit to be gained by combining two drugs. This is important as testing drug combinations in complex biological systems are likely to produce a wide variety of possible response surfaces. It is also shown that for monotone increasing (or decreasing) dose response surfaces that strong nonlinear blending is equivalent to improved potency along a ray of constant dose ratio. This is important because fixed dose ratios form the basis for many preclinical and clinical combination drug experiments. Two examples are given involving HIV and cancer chemotherapy combination drug experiments.

SUMMARY We derive a Monte-Carlo-amenable, minimum variance unbiased estimator of a nonlinear function of a normal mean and the variance of the estimator. Applications to problems arising in the analysis of data measured with error are described.

A series of N-hydroxyformamide tumor necrosis factor-alpha converting enzyme (TACE)/matrix metalloprotease (MMP) inhibitors were evaluated for their potential to induce human cytochrome P450 3A (CYP3A). Two in vitro assays were used: 1) a cell-based reporter gene assay for activation of the pregnane X receptor (PXR), and 2) a primary "sandwich" culture of human hepatocytes. Approximately 50 TACE/MMP inhibitors were evaluated in the human PXR assay. A range of PXR activation was observed, 0 to 150% of the activation of the known human CYP3A inducer rifampicin. Three TACE/MMP inhibitors were evaluated in rat and human hepatocytes. Significantly higher PXR activation/CYP3A induction was observed in PXR/hepatocyte models, respectively, for (2R,3S) 3-(formyl-hydroxyamino)-2-(2-methyl-1-propyl)-4-methylpentanoic acid [(1S,2S)-2-methyl-1-(2-pyridylcarbamoyl)-1-butyl]amide (GW3333) compared with (2R,3S)-6,6,6-trifluoro-3-[formyl(hydroxy)amino]-2-isobutyl-N-[(1S,2R)-2-methoxy-1-[(1,3-thiazol-2-ylamino)carbonyl]propyl]hexanamide (GW6495) and (2R)-N-[(1S)-2,2-dimethyl-1-[(methylamino)carbonyl]-propyl]-2-[(1S)-1-[formyl(hydroxy)amino]ethyl]-5-phenylpentanamide (GI4023). The CYP3A induction level achieved with GW3333 at a concentration of approximately 10 microM in human hepatocytes was comparable to that achieved with rifampicin at a concentration of 10 microM. The extent of rodent CYP3A induction caused by GW3333 was confirmed in vivo after daily oral administration for 14 days to rats. In conclusion, GW3333 is a potential inducer of CYP3A expression in vivo in humans, but other N-hydroxyformamides are less likely to induce CYP3A.

A Monte Carlo method of computing unbiased estimating equations for the analysis of data measured with error is described. Asymptotic distribution results are obtained for estimators derived from the Monte Carlo estimating equations. The method is illustrated with examples, applications, and simulation studies. The Monte Carlo estimating equations are corrected scores in the sense of Nakamura, and the proposed methods are closely related to the simulation method described by Cook and Stefanski.

Dissolution (or in vitro release) studies constitute an important aspect of pharmaceutical drug development. One important use of such studies is for justifying a biowaiver for post-approval changes which requires establishing equivalence between the new and old product. We propose a statistically rigorous modeling approach for this purpose based on the estimation of what we refer to as the F2 parameter, an extension of the commonly used f2 statistic. A Bayesian test procedure is proposed in relation to a set of composite hypotheses that capture the similarity requirement on the absolute mean differences between test and reference dissolution profiles. Several examples are provided to illustrate the application. Results of our simulation study comparing the performance of f2 and the proposed method show that our Bayesian approach is comparable to or in many cases superior to the f2 statistic as a decision rule. Further useful extensions of the method, such as the use of continuous-time dissolution modeling, are considered.

Under the Loewe additivity, constant relative potency between two drugs is a sufficient condition for the two drugs to be additive. Implicit in this condition is that one drug acts like a dilution of the other. Geometrically, it means that the dose-response curve of one drug is a copy of another that is shifted horizontally by a constant over the log-dose axis. Such phenomenon is often referred to as parallelism. Thus, testing drug additivity is equivalent to the demonstration of parallelism between two dose-response curves. Current methods used for testing parallelism are usually based on significance tests for differences between parameters in the dose-response curves of the monotherapies. A p-value of less than 0.05 is indicative of non-parallelism. The p-value-based methods, however, may be fundamentally flawed because an increase in either sample size or precision of the assay used to measure drug effect may result in more frequent rejection of parallel lines for a trivial difference. Moreover, similarity (difference) between model parameters does not necessarily translate into the similarity (difference) between the two response curves. As a result, a test may conclude that the model parameters are similar (different), yet there is little assurance on the similarity between the two dose-response curves. In this paper, we introduce a Bayesian approach to directly test the hypothesis that the two drugs have a constant relative potency. An important utility of our proposed method is in aiding go/no-go decisions concerning two drug combination studies. It is illustrated with both a simulated example and a real-life example.

A method for detecting deviations from the Loewe additive drug combination reference model for in vitro drug combination experimentation is described. It is often difficult to fit a response surface model to drug combination data, especially in situations where the experimental design contains a sparse set of combinations. The literature does contain good response surface modeling approaches, but they tend to be complex and can be difficult to execute. It is especially difficult to check model quality when fitting to more than two combined agents. A simple method based on sound statistical principles is proposed that examines the mean response deviation of each combination from the predicted response under Loewe additivity. The method can readily handle any number of combined agents, does not require sophisticated modeling, and can even be programmed into Microsoft Excel without the use of macros. Several potential extensions to the method are discussed in detail. Computer-generated simulations demonstrate the statistical capabilities of the approach, and a real-data example is given to illustrate the method.

Drug-induced prolongation of the QT interval via hERG block is a major cause of attrition in drug development. The advent of automated electrophysiology systems has greatly reduced this risk by enabling the detection of hERG block earlier in drug discovery. In this study, we have evaluated the suitability of the IonWorks Barracuda for the characterization of hERG biophysics and pharmacology. The IonWorks Barracuda is a second generation automated patch clamp system with increased throughput and capabilities, allowing 384 parallel recordings and up to 8 additions per recording. All experiments were conducted with CHO cells stably expressing hERG. Recordings were made in population patch clamp mode, where each current is measured from an average of up to 64 cells. We first examined hERG gating characteristics. HERG V1/2 for activation was −7.8 ± 1.8 mV (n=183) on the IonWorks Barracuda versus −17.5 ± 5.3 mV (n=9) by manual patch clamp. For pharmacological experiments, all drug additions were made in a cumulative fashion such that up to 384 dose-responses could be obtained in a single experiment. Seal resistances and currents were stable (

The FDA proposed a parametric tolerance interval (PTI) test at the October 2005 Advisory Committee meeting as a replacement of the attribute (counting) test for delivered dose uniformity (DDU), published in the 1998 draft guidance for metered dose inhalers (MDIs) and dry powder inhalers (DPIs) and the 2002 final guidance for inhalation sprays and intranasal products. This article (first in a series of three) focuses on the test named by the FDA “87.5% coverage.” Unlike a typical two-sided PTI test, which controls the proportion of the DDU distribution within a target interval (coverage), this test is comprised of two one-sided tests (TOST) designed to control the maximum amount of DDU values in either tail of the distribution above and below the target interval. Through simulations, this article characterizes the properties and performance of the proposed PTI-TOST under different scenarios. The results show that coverages of 99% or greater are needed for a batch to have acceptance probability 98% or greater with the test named by the FDA “87.5% coverage” (95% confidence level), while batches with 87.5% coverage have less than 1% probability of being accepted. The results also illustrate that with this PTI-TOST, the coverage requirement for a given acceptance probability increases as the batch mean deviates from target. The accompanying articles study the effects of changing test parameters and the test robustness to deviations from normality.

The robustness of the parametric tolerance interval test, which was proposed by the Food and Drug Administration for control of delivered dose uniformity in orally inhaled and nasal drug products, is investigated in this article using different scenarios for deviations from a univariate normal distribution. The studied scenarios span a wide range of conditions, the purpose of which is to provide an understanding of how the test performs depending on the nature and degree of the deviation from normality. Operating characteristic curves were generated to compare the performance of the test for different types of distributions (normal and non-normal) having the same proportion of doses in the tails (on one or both sides) outside the target interval. The results show that, in most cases, non-normality does not increase the probability of accepting a batch of unacceptable quality (i.e., the test is robust) except in extreme situations, which do not necessarily represent commercially viable products. The results also demonstrate that, in the case of bimodal distributions where the life-stage means differ from each other by up to 24% label claim, the test's criterion on life-stage means does not affect pass rates because the tolerance interval portion of the test reacts to shifting means as well.

Clinical trials of the first approved integrase inhibitor (INI), raltegravir, have demonstrated a drop in the human immunodeficiency virus type 1 (HIV-1) RNA loads of infected patients that was unexpectedly more rapid than that with a potent reverse transcriptase inhibitor, and apparently dose independent. These clinical outcomes are not understood. In tissue culture, although their inhibition of integration is well documented, the effects of INIs on levels of unintegrated HIV-1 cDNAs have been variable. Furthermore, there has been no report to date on an INI's effect on these episomal species in vivo. Here, we show that prophylactic treatment of transgenic rats with the strand transfer INI GSK501015 reduced levels of viral integrants in the spleen by up to 99.7%. Episomal two-long-terminal-repeat (LTR) circles accumulated up to sevenfold in this secondary lymphoid organ, and this inversely correlated with the impact on the proviral burden. Contrasting raltegravir's dose-ranging study with HIV patients, titration of GSK501015 in HIV-infected animals demonstrated dependence of the INI's antiviral effect on its serum concentration. Furthermore, the in vivo 50% effective concentration calculated from these data best matched GSK501015's in vitro potency when serum protein binding was accounted for. Collectively, this study demonstrates a titratable, antipodal impact of an INI on integrated and episomal HIV-1 cDNAs in vivo. Based on these findings and known biological characteristics of viral episomes, we discuss how integrase inhibition may result in additional indirect antiviral effects that contribute to more rapid HIV-1 decay in HIV/AIDS patients.