Abstract: The military lifestyle often includes continuous operations whether in training or deployed environments. These stressful environments present unique challenges for service members attempting to achieve consolidated, restorative sleep. The significant mental and physical derangements caused by degraded metabolic, cardiovascular, skeletomuscular, and cognitive health often result from insufficient sleep and/or circadian misalignment. Insufficient sleep and resulting fatigue compromises personal safety, mission success, and even national security. In the long-term, chronic insufficient sleep and circadian rhythm disorders have been associated with other sleep disorders (e.g., insomnia, obstructive sleep apnea, and parasomnias). Other physiologic and psychologic diagnoses such as post-traumatic stress disorder, cardiovascular disease, and dementia have also been associated with chronic, insufficient sleep. Increased co-morbidity and mortality are compounded by traumatic brain injury resulting from blunt trauma, blast exposure, and highly physically demanding tasks under load. We present the current state of science in human and animal models specific to service members during- and post-military career. We focus on mission requirements of night shift work, sustained operations, and rapid re-entrainment to time zones. We then propose targeted pharmacological and non-pharmacological countermeasures to optimize performance that are mission- and symptom-specific. We recognize a critical gap in research involving service members, but provide tailored interventions for military health care providers based on the large body of research in health care and public service workers.

Abstract: Validated measures of animal affect are crucial to research spanning numerous disciplines. Judgement bias, which assesses decision-making under ambiguity, is a promising measure of animal affect. One way of validating this measure is to administer drugs with affect-altering properties in humans to non-human animals and determine whether the predicted judgement biases are observed. We conducted a systematic review and meta-analysis using data from 20 published research articles that use this approach, from which 557 effect sizes were extracted. Pharmacological manipulations overall altered judgement bias at the probe cues as predicted. However, there were several moderating factors including the neurobiological target of the drug, whether the drug induced a relatively positive or negative affective state in humans, dosage, and the presented cue. This may partially reflect interference from adverse effects of the drug which should be considered when interpreting results. Thus, the overall pattern of change in animal judgement bias appears to reflect the affect-altering properties of drugs in humans, and hence may be a valuable measure of animal affective valence.

Abstract: Complement-dependent cytotoxicity (CDC) is a potent effector mechanism, engaging both innate and adaptive immunity. Although strategies to improve the CDC activity of antibody therapeutics have primarily focused on enhancing the interaction between the antibody crystallizable fragment (Fc) and the first subcomponent of the C1 complement complex (C1q), the relative importance of intrinsic affinity and binding valency of an antibody to the target antigen is poorly understood. Here we show that antibody binding affinity to a cell surface target antigen evidently affects the extent and efficacy of antibody-mediated complement activation. We further report the fundamental role of antibody binding valency in the capacity to recruit C1q and regulate CDC. More specifically, an array of affinity-modulated variants and functionally monovalent bispecific derivatives of high-affinity anti-epidermal growth factor receptor (EGFR) and anti-human epidermal growth factor receptor 2 (HER2) therapeutic immunoglobulin Gs (IgGs), previously reported to be deficient in mediating complement activation, were tested for their ability to bind C1q by biolayer interferometry using antigen-loaded biosensors and to exert CDC against a panel of EGFR and HER2 tumor cells of various histological origins. Significantly, affinity-reduced variants or monovalent derivatives, but not their high-affinity bivalent IgG counterparts, induced near-complete cell cytotoxicity in tumor cell lines that had formerly been shown to be resistant to complement-mediated attack. Our findings suggest that monovalent target engagement may contribute to an optimal geometrical positioning of the antibody Fc to engage C1q and deploy the complement pathway.

Abstract: Despite advances in medical care, cancer remains a major threat to human health. Antibody-drug conjugates (ADCs) are a promising targeted therapy to overcome adverse side effects to normal tissues. In this field, the current challenge is obtaining homogeneous preparations of conjugates, where a defined number of drugs are conjugated to specific antibody sites. Site-directed cysteine-based conjugation is commonly used to obtain homogeneous ADC, but it is a time-consuming and expensive approach due to the need for extensive antibody engineering to identify the optimal conjugation sites and reduction - oxidation protocols are specific for each antibody. There is thus a need for ADC platforms that offer homogeneity and direct applicability to the already approved antibody therapeutics. Here we describe a novel approach to derive homogeneous ADCs with drug-to-antibody ratio of 2 from any human immunoglobulin 1 (IgG1), using trastuzumab as a model. The method is based on the production of heavy chains (HC) and light chains (LC) in two recombinant HEK293 independent cultures, so the original amino acid sequence is not altered. Isolated LC was effectively conjugated to a single drug-linker (vcMMAE) construct and mixed to isolated HC dimers, in order to obtain a correctly folded ADC. The relevance of the work was validated in terms of ADC homogeneity (HIC-HPLC, MS), purity (SEC-HPLC), isolated antigen recognition (ELISA) and biological activity (HER2-positive breast cancer cells cytotoxicity assays).

Abstract: Novel treatment regimens are required for castration-resistant prostate cancers (CRPCs) that become unresponsive to standard treatments, such as docetaxel and enzalutamide. Histone deacetylase (HDAC) inhibitors showed promising results in hematological malignancies, but they failed in solid tumors such as prostate cancer, despite the overexpression of HDACs in CRPC. Four HDAC inhibitors, vorinostat, pracinostat, panobinostat and romidepsin, underwent phase II clinical trials for prostate cancers; however, phase III trials were not recommended due to a majority of patients exhibiting either toxicity or disease progression. In this review, the pharmacodynamic reasons for the failure of HDAC inhibitors were assessed and placed in the context of the advancements in the understanding of CRPCs, HDACs and resistance mechanisms. The review focuses on three themes: evolution of androgen receptor-negative prostate cancers, development of resistance mechanisms and differential effects of HDACs. In conclusion, advancements can be made in this field by characterizing HDACs in prostate tumors more extensively, as this will allow more specific drugs catering to the specific HDAC subtypes to be designed.

Abstract: BACKGROUND: Preeclampsia (PE) is a heterogeneous, hypertensive disorder of pregnancy, with no robust biomarkers or effective treatments. PE increases the risk of poor outcomes for both the mother and the baby. Methylation-mediated transcriptional dysregulation motifs (methTDMs) could contribute the PE development. However, precise functional roles of methTDMs in PE have not been globally described. METHODS: Here, we develop a comprehensive and computational pipeline to identify PE-specific methTDMs following TF, gene, methylation expression profile, and experimentally verified TF-gene interactions. RESULTS: The regulation patterns of methTDMs are multiple and complex in PE and contain relax inhibition, intensify inhibition, relax activation, intensify activation, reverse activation, and reverse inhibition. A core module is extracted from global methTDM network to further depict the mechanism of methTDMs in PE. The common and specific features of any two kinds of regulation pattern are also analyzed in PE. Some key methylation sites, TFs, and genes such as IL2RG are identified in PE. Functional analysis shows that methTDMs are associated with immune-, insulin-, and NK cell-related functions. Drug-related network identifies some key drug repurposing candidates such as NADH. CONCLUSION: Collectively, the study highlighted the effect of methylation on the transcription process in PE. MethTDMs could contribute to identify specific biomarkers and drug repurposing candidates for PE.

Abstract: The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

Abstract: Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Abstract: A limiting factor for oral delivery of macromolecules is low intestinal epithelial permeability. 1-Phenylpiperazine (PPZ), 1-(4-methylphenyl) piperazine (1-4-MPPZ) and 1-methyl-4-phenylpiperazine (1-M-4-PPZ) have emerged as potential permeation enhancers (PEs) from a screen carried out by others in Caco-2 monolayers. Here, their efficacy, mechanism of action and potential for epithelial toxicity were further examined in Caco-2 cells and isolated rat intestinal mucosae. Using high-content analysis, PPZ and 1-4-MPPZ decreased mitochondrial membrane potential and increased plasma membrane potential in Caco-2 cells to a greater extent than 1-M-4-PPZ. The Papp of the paracellular marker, [(14)C]-mannitol, and of the peptide, [(3)H]-octreotide, was measured across rat colonic mucosae following apical addition of the three piperazines. PPZ and 1-4-MPPZ induced a concentration-dependent decrease in transepithelial electrical resistance (TEER) and an increase in the Papp of [(14)C]-mannitol without causing histological damage. 1-M-4-PPZ was without effect. The piperazines caused the Krebs-Henseleit buffer pH to become alkaline, which partially attenuated the increase in Papp of [(14)C]-mannitol caused by PPZ and 1-4-MPPZ. Only addition of 1-4-MPPZ increased the Papp of [(3)H]-octreotide. Pre-incubation of mucosae with two 5-HT4 receptor antagonists, a loop diuretic and a myosin light chain kinase inhibitor, reduced the permeation enhancement capacity of PPZ and 1-4-MPP for [(14)C]-mannitol. 1-4-MPPZ holds most promise as a PE, but intestinal physiology may also be impacted due to multiple mechanisms of action.

Abstract: Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology.