Abstract: Naproxen (NPX) is a nonsteroidal anti-inflammatory drug with pain- and fever-relieving properties, currently marketed in the sodium salt form to overcome solubility problems; however, alternative solutions for improving its solubility across all pH values are desirable. NPX is suitable for cocrystal formation, with hydrogen-bonding possibilities via the COOH group. The crystal structure is presented of a 1:1 cocrystal of NPX with picolinamide as a coformer [systematic name: (S)-2-(6-methoxynaphthalen-2-yl)propanoic acid-pyridine-2-carboxamide (1/1), C14H14O3.C6H6N2O]. The pharmaceutically relevant physical properties were investigated and the intrinsic dissolution rate was found to be essentially the same as that of commercial naproxen. An NMR crystallography approach was used to investigate the H-atom positions in the two crystallographically unique COOH-CONH hydrogen-bonded dimers. (1)H solid-state NMR distinguished the two carboxyl protons, despite the very similar crystallographic environments. The nature of the hydrogen bonding was confirmed by solid-state NMR and density functional theory calculations.

Abstract: BACKGROUND: Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs. RESULTS: The proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study. CONCLUSION: Our proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development.

Abstract: Acetaminophen (APAP) is a widely used analgesic drug that is frequently co-administered with caffeine (CAF) in the treatment of pain. It is well known that APAP may cause severe liver injury after an acute overdose. However, the understanding of whether and to what extent CAF inhibits or stimulates APAP-induced hepatotoxicity in humans is still lacking. Here, a multiscale analysis is presented that quantitatively models the pharmacodynamic (PD) response of APAP during co-medication with CAF. Therefore, drug-drug interaction (DDI) processes were integrated into physiologically based pharmacokinetic (PBPK) models at the organism level, whereas drug-specific PD response data were contextualized at the cellular level. The results provide new insights into the inhibitory and stimulatory effects of CAF on APAP-induced hepatotoxicity for crucially affected key cellular processes and individual genes at the patient level. This study might facilitate the risk assessment of drug combination therapies in humans and thus may improve patient safety in clinical practice.

Abstract: Opioids and non-steroidal anti-inflammatory drugs are used commonly to manage pain in the early phase of spinal cord injury (SCI). Despite its analgesic efficacy, however, our studies suggest that intrathecal morphine undermines locomotor recovery and increases lesion size in a rodent model of SCI. Similarly, intravenous (IV) morphine attenuates locomotor recovery. The current study explores whether IV morphine also increases lesion size after a spinal contusion (T12) injury and quantifies the cell types that are affected by early opioid administration. Using an experimenter-administered escalating dose of IV morphine across the first seven days post-injury, we quantified the expression of neuron, astrocyte, and microglial markers at the injury site. SCI decreased NeuN expression relative to shams. In subjects with SCI treated with IV morphine, virtually no NeuN(+) cells remained across the rostral-caudal extent of the lesion. Further, whereas SCI per se increased the expression of astrocyte and microglial markers (glial fibrillary acidic protein and OX-42, respectively), morphine treatment decreased the expression of these markers. These cellular changes were accompanied by attenuation of locomotor recovery (Basso, Beattie, Bresnahan scores), decreased weight gain, and the development of opioid-induced hyperalgesia (increased tactile reactivity) in morphine-treated subjects. These data suggest that morphine use is contraindicated in the acute phase of a spinal injury. Faced with a lifetime of intractable pain, however, simply removing any effective analgesic for the management of SCI pain is not an ideal option. Instead, these data underscore the critical need for further understanding of the molecular pathways engaged by conventional medications within the pathophysiological context of an injury.

Abstract: Colorectal cancer remains the third leading cause of cancer death worldwide, suggesting exploration of novel therapeutic avenues may be useful. In this study, therefore, we determined whether Tianfoshen oral liquid, a Chinese traditional medicine that has been used to treat non-small cell lung cancer, would be therapeutically beneficial for colorectal cancer patients. Our data show that Tianfoshen oral liquid effectively inhibits growth of colorectal cancer cells both in vitro and in vivo. We further employed a comprehensive strategy that included chemoinformatics, bioinformatics and network biology methods to unravel novel insights into the active compounds of Tianfoshen oral liquid and to identify the common therapeutic targets and processes for colorectal cancer treatment. We identified 276 major candidate targets for Tianfoshen oral liquid that are central to colorectal cancer progression. Gene enrichment analysis showed that these targets were associated with cell cycle, apoptosis, cancer-related angiogenesis, and chronic inflammation and related signaling pathways. We also validated experimentally the inhibitory effects of Tianfoshen oral liquid on these pathological processes, both in vitro and in vivo. In addition, we demonstrated that Tianfoshen oral liquid suppressed multiple relevant key players that sustain and promote colorectal cancer, which is suggests the potential therapeutic efficacy of Tianfoshen oral liquid in future colorectal cancer treatments.

Abstract: Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.

Abstract: Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration-time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development.

Abstract: BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response ( http://databio.gachon.ac.kr/tools/ ). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

Abstract: The binding mode prediction is of great importance to structure-based drug design. The discrimination of various binding poses of ligand generated by docking is a great challenge not only to docking score functions but also to the relatively expensive free energy calculation methods. Here we systematically analyzed the stability of various ligand poses under molecular dynamics (MD) simulation. First, a data set of 120 complexes was built based on the typical physicochemical properties of drug-like ligands. Three potential binding poses (one correct pose and two decoys) were selected for each ligand from self-docking in addition to the experimental pose. Then, five independent MD simulations for each pose were performed with different initial velocities for the statistical analysis. Finally, the stabilities of ligand poses under MD were evaluated and compared with the native one from crystal structure. We found that about 94% of the native poses were maintained stable during the simulations, which suggests that MD simulations are accurate enough to judge most experimental binding poses as stable properly. Interestingly, incorrect decoy poses were maintained much less and 38-44% of decoys could be excluded just by performing equilibrium MD simulations, though 56-62% of decoys were stable. The computationally-heavy binding free energy calculation can be performed only for these survived poses.

Abstract: Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application.