A review of synergy concepts of nonlinear blending and dose-reduction profiles.

This article presents a case-study review of synergy concepts of nonlinear blending and dose-reduction profiles. "Strong nonlinear blending" is a novel concept that provides a flexible paradigm for the assessment of combination drug synergy that is applicable to any shaped combination-drug dose-response surface; issues of varying relative potency, partial inhibitors, potentiation, or coalism pose no problems at all. Dose-reduction profiles are overlay plots created to show how much each drug can be reduced in amount and yet achieve the same efficacy as larger amounts of each drug used individually. This review applies these synergy concepts to two data sets from a previously published experiment. The previous publication had claimed a high degree of Loewe synergy for one of the data sets. However, a more penetrating analysis shows that with regard to strong nonlinear blending there is no reason to blend (for purposes of response enhancement) the two compounds studied. However, the dose-reduction profile plots show how Loewe synergy is present and provide further insight to the interaction of the two compounds (on the dose-concentration scale).

The identification of a novel phosphodiesterase 4 inhibitor, 1-ethyl-5-{5-[(4-methyl-1-piperazinyl)methyl]-1,3,4-oxadiazol-2-yl}-N-(tetrahydro-2H-pyran-4-yl)-1H-pyrazolo[3,4-b]pyridin-4-amine (EPPA-1), with improved therapeutic index using pica feeding in rats as a measure of emetogenicity.

Clinical utility of phosphodiesterase 4 (PDE4) inhibitors as anti-inflammatory agents has, to date, been limited by adverse effects including nausea and emesis, making accurate assessment of emetic versus anti-inflammatory potencies critical to the development of inhibitors with improved therapeutic indices. In the present study we determined the in vitro and in vivo anti-inflammatory potencies of the first-generation PDE4 inhibitor, rolipram, the second-generation inhibitors, roflumilast and cilomilast, and a novel third generation inhibitor, 1-ethyl-5-{5-[(4-methyl-1-piperazinyl)methyl]-1,3,4-oxadiazol-2-yl}-N-(tetrahydro-2H-pyran-4-yl)-1H-pyrazolo[3,4-b]pyridin-4-amine (EPPA-1). The rank-order potency against lipopolysaccharide (LPS)-induced tumor necrosis factor-alpha production by human peripheral blood mononuclear cells was roflumilast (IC(50) = 5 nM) > EPPA-1 (38) > rolipram (269) > cilomilast (389), and against LPS-induced pulmonary neutrophilia in the rat was EPPA-1 (D(50) = 0.042 mg/kg) > roflumilast (0.24) > rolipram (3.34) > cilomilast (4.54). Pica, the consumption of non-nutritive substances in response to gastrointestinal stress, was used as a surrogate measure for emesis, giving a rank-order potency of rolipram (D(50) = 0.495 mg/kg) > roflumilast (1.6) > cilomilast (6.4) > EPPA-1 (24.3). The low and high emetogenic activities of EPPA-1 and rolipram, respectively, detected in the pica model were confirmed in a second surrogate model of emesis, reversal of alpha(2)-adrenoceptor-mediated anesthesia in the mouse. The rank order of therapeutic indices derived in the rat [(pica D(50))/(neutrophilia D(50))] was EPPA-1 (578) > roflumilast (6.4) > cilomilast (1.4) > rolipram (0.15), consistent with the rank order derived in the ferret [(emesis D(50))/(neutrophilia D(50))]. These data validate rat pica feeding as a surrogate for PDE4 inhibitor-induced emesis in higher species, and identify EPPA-1 as a novel PDE4 inhibitor with an improved therapeutic index.

A Bayesian approach to the ICH Q8 definition of design space.

Manufacturers of pharmaceuticals and biopharmaceuticals are facing increased regulatory pressure to understand how their manufacturing processes work and to be able to quantify the reliability and robustness of their manufacturing processes. In particular, the ICH Q8 guidance has introduced the concept of design space. The ICH Q8 defines design space as "the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality." However, relatively little has been put forth to date on how to construct a design space from data composed of such variables. This study presents a Bayesian approach to design space based upon a type of credible region first appearing in Peterson's work.This study considers the issues of constructing a Bayesian design space, design space reliability, the inclusion of process noise variables, and utilization of prior information, as well as an outline for organizing information about a design space so that manufacturing engineers can make informed changes as may be needed within the design space.

Inhibition of invariant chain processing, antigen-induced proliferative responses, and the development of collagen-induced arthritis and experimental autoimmune encephalomyelitis by a small molecule cysteine protease inhibitor.

Members of the papain family of cysteine proteases (cathepsins) mediate late stage processing of MHC class II-bound invariant chain (Ii), enabling dissociation of Ii, and binding of antigenic peptide to class II molecules. Recognition of cell surface class II/Ag complexes by CD4(+) T cells then leads to T cell activation. Herein, we demonstrate that a pan-active cathepsin inhibitor, SB-331750, attenuated the processing of whole cell Ii p10 to CLIP by Raji cells, and DBA/1, SJL/J, and C57BL/6 splenocytes. In Raji cells and C57BL/6 splenocytes, SB-331750 inhibited class II-associated Ii processing and reduced surface class II/CLIP expression, whereas in SB-331750-treated DBA/1 and SJL/J splenocytes, class II-associated Ii processing intermediates were undetectable. Incubation of lymph node cells/splenocytes from collagen-primed DBA/1 mice and myelin basic protein-primed SJL/J mice with Ag in the presence of SB-331750 resulted in concentration-dependent inhibition of Ag-induced proliferation. In vivo administration of SB-331750 to DBA/1, SJL/J, and C57BL/6 mice inhibited splenocyte processing of whole cell Ii p10 to CLIP. Prophylactic administration of SB-331750 to collagen-immunized/boosted DBA/1 mice delayed the onset and reduced the severity of collagen-induced arthritis (CIA), and reduced paw tissue levels of IL-1beta and TNF-alpha. Similarly, treatment of myelin basic protein-primed SJL/J lymph node cells with SB-331750 delayed the onset and reduced the severity of adoptively transferred experimental autoimmune encephalomyelitis (EAE). Therapeutic administration of SB-331750 reduced the severity of mild/moderate CIA and EAE. These results indicate that pharmacological inhibition of cathepsins attenuates CIA and EAE, potentially via inhibition of Ii processing, and subsequent Ag-induced T cell activation.

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.

Probabilistic index: an intuitive non-parametric approach to measuring the size of treatment effects.

Effect sizes (ES) tell the magnitude of the difference between treatments and, ideally, should tell clinicians how likely their patients will benefit from the treatment. Currently used ES are expressed in statistical rather than in clinically useful terms and may not give clinicians the appropriate information. We restrict our discussion to studies with two groups: one with n patients receiving a new treatment and the other with m patients receiving the usual or no treatment. The standardized mean difference (e.g. Cohen's d) is a well-known index for continuous outcomes. There is some intuitive value to d, but measuring improvement in standard deviations (SD) is a statistical concept that may not help a clinician. How much improvement is a half SD? A more intuitive and simple-to-calculate ES is the probability that the response of a patient given the new treatment (X) is better than the one for a randomly chosen patient given the old or no treatment (Y) (i.e. P(X > Y), larger values meaning better outcomes). This probability has an immediate identity with the area under the curve (AUC) measure in procedures for receiver operator characteristic (ROC) curve comparing responses to two treatments. It also can be easily calculated from the Mann-Whitney U, Wilcoxon, or Kendall tau statistics. We describe the characteristics of an ideal ES. We propose P(X > Y) as an alternative index, summarize its correspondence with well-known non-parametric statistics, compare it to the standardized mean difference index, and illustrate with clinical data.

A general approach to confidence regions for optimal factor levels of response surfaces.

For a response surface experiment, an approximate hypothesis test and an associated confidence region is proposed for the minimizing (or maximizing) factor-level configuration. Carter et al. (1982, Cancer Research 42, 2963-2971) show that confidence regions for optimal conditions provide a way to make decisions about therapeutic synergism. The response surface may be constrained to be within a specified, bounded region. These constraint regions can be quite general. This allows for more realistic constraint modeling and a wide degree of applicability, including constraints occurring in mixture experiments. The usual assumption of a quadratic model is also generalized to include any regression model that is linear in the model parameters. An intimate connection is established between this confidence region and the Box-Hunter (1954, Biometrika 41, 190-199) confidence region for a stationary point. As a byproduct, this methodology also provides a way to construct a confidence interval for the difference between the optimal mean response and the mean response at a specified factor-level configuration. The application of this confidence region is illustrated with two examples. Extensive simulations indicate that this confidence region has good coverage properties.