Steven Novick

A simple method for quantifying functional selectivity and agonist bias

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.

Nonlinear Blending: A Useful General Concept for the Assessment of Combination Drug Synergy

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.

A Two One-Sided Parametric Tolerance Interval Test for Control of Delivered Dose Uniformity. Part 1—Characterization of FDA Proposed Test

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.

In Vitro Dissolution Curve Comparisons: A Critique of Current Practice

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.

A simple test for synergy for a small number of combinations.

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.

A Bayesian Approach to Parallelism Testing in Bioassay

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 testing the hypothesis that the test sample is a dilution or concentration of the reference standard. We propose a Bayesian approach to directly test the hypothesis. When the dose–response curves are linear, a closed-form solution is obtained. For nonlinear cases, we render a solution based on a simple two-dimensional optimization routine. The empirical properties of the method are evaluated and compared with existing methods through a simulation study based on real-life examples. It is shown that the method overcomes the shortcomings of the current approaches and is a viable alternative to parallelism testing.

Pharmacokinetics/Pharmacodynamics of Peptide Deformylase Inhibitor GSK1322322 against Streptococcus pneumoniae, Haemophilus influenzae, and Staphylococcus aureus in Rodent Models of Infection

ABSTRACT 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. pneumoniae, 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 (fAUC/MIC) index in S. pneumoniae (R2, 0.83), whereas fAUC/MIC and free maximum drug concentration (fCmax)/MIC were the best efficacy predictors for S. aureus (R2, 0.9 and 0.91, respectively). Median daily fAUC/MIC values required for stasis and for a 1-log10 reduction in bacterial burden were 8.1 and 14.4 for 11 S. pneumoniae isolates (R2, 0.62) and 7.2 and 13.0 for five H. influenzae isolates (R2, 0.93). The data showed that for eight S. aureus isolates, fAUC correlated better with efficacy than fAUC/MIC (R2, 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 fAUCs of 2.1 and 6.3 μg · h/ml were associated with stasis and 1-log10 reductions, respectively, for S. aureus.

A Two One-Sided Parametric Tolerance Interval Test for Control of Delivered Dose Uniformity—Part 3—Investigation of 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.

Testing drug additivity based on monotherapies

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.

Data-Driven Prior Distributions for A Bayesian Phase-2 COPD Dose-Finding Clinical Trial

ABSTRACT 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.