Nonclassical Phenyl Bioisosteres as Effective Replacements in a Series of Novel Open-Source Antimalarials

The replacement of one chemical motif with another that is broadly similar is a common method in medicinal chemistry to modulate the physical and biological properties of a molecule (i.e., bioisosterism). In recent years, bioisosteres such as cubane and bicyclo[1.1.1]pentane (BCP) have been used as highly effective phenyl mimics. Herein, we show the successful incorporation of a range of phenyl bioisosteres during the open-source optimization of an antimalarial series. Cubane () and -carborane () analogues exhibited improved potency against compared to the parent phenyl compound; however, these changes resulted in a reduction in metabolic stability; unusually, enzyme-mediated oxidation was found to take place on the cubane core. A BCP analogue () was found to be equipotent to its parent phenyl compound and showed significantly improved metabolic properties. While these results demonstrate the utility of these atypical bioisosteres when used in a medicinal chemistry program, the search to find a suitable bioisostere may well require the preparation of many candidates, in our case, 32 compounds

Linking in vitro lipolysis and microsomal metabolism for the quantitative prediction of oral bioavailability of BCS II drugs administered in lipidic formulations

Lipidic formulations (LFs) are increasingly utilized for the delivery of drugs that belong to class II of the Biopharmaceutics Classification System (BCS). The current work proposes, for the first time, the combination of in vitro lipolysis and microsomal metabolism studies for the quantitative prediction of human oral bioavailability of BCS II drugs administered in LFs. Marinol® and Neoral® were selected as model LFs and their observed oral bioavailabilities (Fobserved) obtained from published clinical studies in humans. Two separate lipolysis buffers, differing in the level of surfactant concentrations, were used for digestion of the LFs. The predicted fraction absorbed (Fabs) was calculated by measuring the drug concentration in the micellar phase after completion of the lipolysis process. To determine first-pass metabolism (Fg∙Fh), drug depletion studies with human microsomes were performed. Clearance values were determined by applying the “in vitro half-life approach”. The estimated Fabs and Fg∙Fh values were combined for the calculation of the predicted oral bioavailability (Fpredicted). Results showed that there was a strong correlation between Fobserved and Fpredicted values only when Fabs was calculated using a buffer with surfactant concentrations closer to physiological conditions. The general accuracy of the predicted values suggests that the novel in vitro lipolysis/metabolism approach could quantitatively predict the oral bioavailability of lipophilic drugs administered in LFs

Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

Abstract In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

What are the most efficacious treatment regimens for isoniazid-resistant tuberculosis?:A systematic review and network meta-analysis

INTRODUCTION: Consensus on the best treatment regimens for patients with isoniazid-resistant TB is limited; global treatment guidelines differ. We undertook a systematic review and meta-analysis using mixed-treatment comparisons methodology to provide an up-to-date summary of randomised controlled trials (RCTs) and relative regimen efficacy. METHODS: Ovid MEDLINE, the Web of Science and EMBASE were mined using search terms for TB, drug therapy and RCTs. Extracted data were inputted into fixed-effects and random-effects models. ORs for all possible network comparisons and hierarchical rankings for different regimens were obtained. RESULTS: 12 604 records were retrieved and 118 remained postextraction, representing 59 studies-27 standalone and 32 with multiple papers. In comparison to a baseline category that included the WHO-recommended regimen for countries with high levels of isoniazid resistance (rifampicin-containing regimens using fewer than three effective drugs at 4 months, in which rifampicin was protected by another effective drug at 6 months, and rifampicin was taken for 6 months), extending the duration of rifampicin and increasing the number of effective drugs at 4 months lowered the odds of unfavourable outcomes (treatment failure or the lack of microbiological cure; relapse post-treatment; death due to TB) in a fixed-effects model (OR 0.31 (95% credible interval 0.12-0.81)). In a random-effects model all estimates crossed the null. CONCLUSIONS: Our systematic review and network meta-analysis highlight a regimen category that may be more efficacious than the WHO population level recommendation, and identify knowledge gaps where data are sparse. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42014015025

Identification of Protein Targets of Reactive Metabolites of Tienilic Acid in Human Hepatocytes

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemical Research in Toxicology, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://pubs.acs.org/doi/abs/10.1021/tx300103jTienilic acid (TA) is a uricosuric diuretic that was withdrawn from the market only months after its introduction because of reports of serious incidents of drug-induced liver injury including some fatalities. Its hepatotoxicity is considered to be primarily immunoallergic in nature. Like other thiophene compounds, TA undergoes biotransformation to a S-oxide metabolite which then reacts covalently with cellular proteins. To identify protein targets of TA metabolites, we incubated [14C]-TA with human hepatocytes, separated cellular proteins by 2D gel electrophoresis, and analyzed proteins in 36 radioactive spots by tryptic digestion followed by LC-MS/MS. Thirty one spots contained at least one identifiable protein. Sixteen spots contained only one of 14 non-redundant proteins which were thus considered to be targets of TA metabolites. Six of the 14 were also found in other radioactive spots that contained from 1 to 3 additional proteins. Eight of the 14 had not been reported to be targets for any reactive metabolite other than TA. The other 15 spots each contained from 2–4 identifiable proteins, many of which are known targets of other chemically reactive metabolites, but since adducted peptides were not observed, the identity of the adducted protein(s) in these spots is ambiguous. Interestingly, all the radioactive spots corresponded to proteins of low abundance, while many highly abundant proteins in the mixture showed no radioactivity. Furthermore, of approximately 16 previously reported protein targets of TA in rat liver (Methogo, R., Dansette, P. and Klarskov, K. (2007) Int. J. Mass Spectrom., 268, 284–295), only one (fumarylacetoacetase) is among the 14 targets identified in this work. One reason for this difference may be statistical, given that each study identified a small number of targets from among thousands present in hepatocytes. Another may be the species difference (i.e. rat vs. human), and still another may be the method of detection of adducted proteins (i.e. Western blot vs. C-14). Knowledge of human target proteins is very limited. Of more than 350 known protein targets of reactive metabolites, only 42 are known from human and only 21 of these are known to be targets for more than one chemical. Nevertheless, the demonstration that human target proteins can be identified using isolated hepatocytes in vitro should enable the question of species differences to be addressed more fully in the future

Synergistic drug-cytokine induction of hepatocellular death as an in vitro approach for the study of inflammation-associated idiosyncratic drug hepatotoxicity

Idiosyncratic drug hepatotoxicity represents a major problem in drug development due to inadequacy of current preclinical screening assays, but recently established rodent models utilizing bacterial LPS co-administration to induce an inflammatory background have successfully reproduced idiosyncratic hepatotoxicity signatures for certain drugs. However, the low-throughput nature of these models renders them problematic for employment as preclinical screening assays. Here, we present an analogous, but high-throughput, in vitro approach in which drugs are administered to a variety of cell types (primary human and rat hepatocytes and the human HepG2 cell line) across a landscape of inflammatory contexts containing LPS and cytokines TNF, IFNγ, IL-1α, and IL-6. Using this assay, we observed drug–cytokine hepatotoxicity synergies for multiple idiosyncratic hepatotoxicants (ranitidine, trovafloxacin, nefazodone, nimesulide, clarithromycin, and telithromycin) but not for their corresponding non-toxic control compounds (famotidine, levofloxacin, buspirone, and aspirin). A larger compendium of drug–cytokine mix hepatotoxicity data demonstrated that hepatotoxicity synergies were largely potentiated by TNF, IL-1α, and LPS within the context of multi-cytokine mixes. Then, we screened 90 drugs for cytokine synergy in human hepatocytes and found that a significantly larger fraction of the idiosyncratic hepatotoxicants (19%) synergized with a single cytokine mix than did the non-hepatotoxic drugs (3%). Finally, we used an information theoretic approach to ascertain especially informative subsets of cytokine treatments for most highly effective construction of regression models for drug- and cytokine mix-induced hepatotoxicities across these cell systems. Our results suggest that this drug–cytokine co-treatment approach could provide a useful preclinical tool for investigating inflammation-associated idiosyncratic drug hepatotoxicity.Pfizer Inc.Institute for Collaborative BiotechnologiesMIT Center for Cell Decision ProcessesNational Institute of Mental Health (U.S.) (grant P50-GM68762)National Institute of Mental Health (U.S.) (grant T32-GM008334)Massachusetts Institute of Technology. Biotechnology Process Engineering CenterMassachusetts Institute of Technology. Center for Environmental Health SciencesNational Institute of Mental Health (U.S.) (grant U19ES011399)Whitaker Foundatio

Improved Outcome Prediction Using CT Angiography in Addition to Standard Ischemic Stroke Assessment: Results from the STOPStroke Study

Purpose: To improve ischemic stroke outcome prediction using imaging information from a prospective cohort who received admission CT angiography (CTA). Methods: In a prospectively designed study, 649 stroke patients diagnosed with acute ischemic stroke had admission NIH stroke scale scores, noncontrast CT (NCCT), CTA, and 6-month outcome assessed using the modified Rankin scale (mRS) scores. Poor outcome was defined as mRS.2. Strokes were classified as ‘‘major’ ’ by the (1) Alberta Stroke Program Early CT Score (ASPECTS+) if NCCT ASPECTS was#7; (2) Boston Acute Stroke Imaging Scale (BASIS+) if they were ASPECTS+ or CTA showed occlusion of the distal internal carotid, proximal middle cerebral, or basilar arteries; and (3) NIHSS for scores.10. Results: Of 649 patients, 253 (39.0%) had poor outcomes. NIHSS, BASIS, and age, but not ASPECTS, were independent predictors of outcome. BASIS and NIHSS had similar sensitivities, both superior to ASPECTS (p,0.0001). Combining NIHSS with BASIS was highly predictive: 77.6 % (114/147) classified as NIHSS.10/BASIS+ had poor outcomes, versus 21.5 % (77/358) with NIHSS#10/BASIS2 (p,0.0001), regardless of treatment. The odds ratios for poor outcome is 12.6 (95 % CI: 7.9 to 20.0