Identification Of Novel Drugs Research Articles

Transcriptomics-Based Drug Repurposing Approach Identifies Novel Drugs against Sorafenib-Resistant Hepatocellular Carcinoma.

Simple SummaryHepatocellular carcinoma (HCC), a type of liver cancer, remains a treatment challenge due to late detection and resistance to currently approved drugs. It takes 15–20 years for a single new drug to become FDA approved. The purpose of this study was to expedite identification of novel drugs against drug-resistant HCC. For this, we matched gene expression alterations in resistant HCC with gene expression changes caused by treatment of cancer cells with drugs already FDA approved for other diseases to find the drug that can reverse the resistance-related changes. Among the identified drugs, we validated the growth inhibitory effect of two drugs, identified their mechanism in HCC and, thus, provided proof of concept evidence for validity of this drug repurposing approach with potential for use in personalized medicine.Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. Methods: We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Results: Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. Conclusion: We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine.

Potential Therapeutic Approaches to Alzheimer's Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools.

BackgroundAlzheimer’s disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects.MethodsHere, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structure-biological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aβ), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine.Results In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aβ inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aβ, but their efficiency in vivo and their correct dosage should be determined.ConclusionBioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

PT101 - In silico screening for identification of novel drugs for reprogramming of lineage plasticity in neuroendocrine prostate cancer

PT101 - In silico screening for identification of novel drugs for reprogramming of lineage plasticity in neuroendocrine prostate cancer

DTiGEMS+: drug\u2013target interaction prediction using graph embedding, graph mining, and similarity-based techniques

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

Antimycobacterial Activity of Laurinterol and Aplysin from Laurencia johnstonii.

Marine environments represent a great opportunity for the discovery of compounds with a wide spectrum of bioactive properties. Due to their large variety and functions derived from natural selection, marine natural products may allow the identification of novel drugs based not only on newly discovered bioactive metabolites but also on already known compounds not yet thoroughly investigated. Since drug resistance has caused an increase in infections by Mycobacterium tuberculosis and nontuberculous mycobacteria, the re-evaluation of known bioactive metabolites has been suggested as a good approach to addressing this problem. In this sense, this study presents an evaluation of the in vitro effect of laurinterol and aplysin, two brominated sesquiterpenes isolated from Laurencia johnstonii, against nine M. tuberculosis strains and six nontuberculous mycobacteria (NTM). Laurinterol exhibited good antimycobacterial activity, especially against nontuberculous mycobacteria, being remarkable its effect against Mycobacterium abscessus, with minimum inhibitory concentration (MIC) values lower than those of the reference drug imipenem. This study provides further evidence for the antimycobacterial activity of some sesquiterpenes from L. johnstonii, which can be considered interesting lead compounds for the discovery of novel molecules to treat NTM infections.

TCMIO: A Comprehensive Database of Traditional Chinese Medicine on Immuno-Oncology.

Advances in immuno-oncology (IO) are making immunotherapy a powerful tool for cancer treatment. With the discovery of an increasing number of IO targets, many herbs or ingredients from traditional Chinese medicine (TCM) have shown immunomodulatory function and antitumor effects via targeting the immune system. However, knowledge of underlying mechanisms is limited due to the complexity of TCM, which has multiple ingredients acting on multiple targets. To address this issue, we present TCMIO, a comprehensive database of Traditional Chinese Medicine on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. Over 120,000 small molecules against 400 IO targets were extracted from public databases and the literature. These ligands were further mapped to the chemical ingredients of TCM to identify herbs that interact with the IO targets. Furthermore, we applied a network inference-based approach to identify the potential IO targets of natural products in TCM. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database. Chemical structure mining tools are provided to explore the chemical ingredients and ligands against IO targets. Herb–ingredient–target networks can be generated online, and pathway enrichment analysis for TCM or prescription is available. This database is functional for chemical ingredient structure mining and network analysis for TCM. We believe that this database provides a comprehensive resource for further research on the exploration of the mechanisms of TCM in cancer immunity and TCM-inspired identification of novel drug leads for cancer immunotherapy. TCMIO can be publicly accessed at http://tcmio.xielab.net.

Modeling of Aniridia‐related keratopathy and identification of novel drugs for potential treatment

AbstractHaploinsufficiency of PAX6 in humans is the main cause of congenital aniridia, a rare eye disease characterized by iris hypoplasia and reduced visual acuity. Patients have also progressive disorders including cataract, glaucoma and corneal abnormalities making their condition very challenging to manage. Aniridia‐related keratopathy (ARK), caused by a combination of factors including limbal stem‐cell deficiency, impaired healing response, abnormal differentiation, and infiltration of conjunctival cells onto the corneal surface, affects up to 95% of patients. It usually begins in the first decade of life resulting in recurrent corneal erosions, sub‐epithelial fibrosis with corneal decompensation and opacification. Unfortunately, current treatment options for aniridia patients are currently limited. Although animal models partially recapitulate this disease, there is no in vitro cellular model of AKT needed for drug/therapeutic tools screening and validation. We used genome editing to introduce a nonsense mutation into one allele of the PAX6 gene in limbal cells, a reservoir of stem cells able to differentiate into corneal cells. The mutated cells displayed reduced cell proliferation and cell migration but enhanced cell adhesion. Known PAX6 targets expression was also altered. By High Throughput Screening of a FDA‐approved library, we identified two small compounds that rescued both PAX6 endogenous protein level and altered cell migration of the mutant limbal stem cells. Since these two compounds are already used in psychiatric medicine, their repurposed use to treat topically aniridia eye could become realistic as soon as validated in vivo for efficacy and safety on aniridia‐like mice.

Streamlining drug discovery assays for cardiovascular disease using zebrafish

ABSTRACTIntroduction: In the last decade, our armamentarium of cardiovascular drug therapy has expanded significantly. Using innovative functional genomics strategies such as genome editing by CRISPR/Cas9 as well as high-throughput assays to identify bioactive small chemical compounds has significantly facilitated elaboration of the underlying pathomechanism in various cardiovascular diseases. However, despite scientific progress approvals for cardiovascular drugs has stagnated significantly compared to other fields of drug discovery and therapy during the past years.Areas covered: In this review, the authors discuss the aspects and pitfalls during the early phase of cardiovascular drug discovery and describe the advantages of zebrafish as an in vivo organism to model human cardiovascular diseases (CVD) as well as an in vivo platform for high-throughput chemical compound screening. They also highlight the emerging, promising techniques of automated read-out systems during high-throughput screening (HTS) for the evaluation of important cardiac functional parameters in zebrafish with the potential to streamline CVD drug discovery.Expert opinion: The successful identification of novel drugs to treat CVD is a major challenge in modern biomedical and clinical research. In this context, the definition of the etiologic fundamentals of human cardiovascular diseases is the prerequisite for an efficient and straightforward drug discovery.

Current and Future Horizons of Patient-Derived Xenograft Models in Colorectal Cancer Translational Research.

Our poor understanding of the intricate biology of cancer and the limited availability of preclinical models that faithfully recapitulate the complexity of tumors are primary contributors to the high failure rate of novel therapeutics in oncology clinical studies. To address this need, patient-derived xenograft (PDX) platforms have been widely deployed and have reached a point of development where we can critically review their utility to model and interrogate relevant clinical scenarios, including tumor heterogeneity and clonal evolution, contributions of the tumor microenvironment, identification of novel drugs and biomarkers, and mechanisms of drug resistance. Colorectal cancer (CRC) constitutes a unique case to illustrate clinical perspectives revealed by PDX studies, as they overcome limitations intrinsic to conventional ex vivo models. Furthermore, the success of molecularly annotated "Avatar" models for co-clinical trials in other diseases suggests that this approach may provide an additional opportunity to improve clinical decisions, including opportunities for precision targeted therapeutics, for patients with CRC in real time. Although critical weaknesses have been identified with regard to the ability of PDX models to predict clinical outcomes, for now, they are certainly the model of choice for preclinical studies in CRC. Ongoing multi-institutional efforts to develop and share large-scale, well-annotated PDX resources aim to maximize their translational potential. This review comprehensively surveys the current status of PDX models in translational CRC research and discusses the opportunities and considerations for future PDX development.

Need for sustainable approaches in antileishmanial drug discovery.

Leishmaniasis is a neglected parasitic disease for which the current antileishmania therapeutics are hampered by drug toxicity, high cost, need for parenteral administration, increasing treatment failure rates, and emergence of drug resistance. The R&D pipeline had run fairly dry for several years, but fortunately some new drug candidates are now under (pre)clinical development. Identification of novel drugs will nevertheless remain essential to adequately sustain and improve effective disease control in the future. In this review, a package of standard and accessible R&D approaches is discussed with expansion to some alternative strategies focusing on parasite-host and vector-host interactions.

Abstract 4804: Pre-clinical development of ISOX; A novel therapy for pancreatic cancer identified utilizing system biology approach(s)

Abstract Background: Pancreatic Cancer (PC) remains one of the most common causes of cancer-related deaths. The low survival percentage in patients can be attributed primarily to ineffective therapeutic targeting and lack of new potent therapies. Over the years the conventional one target at a time approach for drug development has failed to achieve promising survival and better approaches are needed. Large-scale genomic database connectivity map (CMAP), a collection of perturbagen profiles from >2800 drugs is a potential tool to identify effective therapies for PC by identifying novel drugs that essentially reverse the global gene signature originating due to malignant PC. Considering this information, we hypothesize that “a big data approach using CMAP will lead to the identification of novel drugs for highly lethal PC.” Methods and Results: To generate a meta-gene signature for PC, GEO data for PC cases (N=106) and normal samples (N=68) was extracted, normalized & assessed for differential gene expression using R Bioconductor. Drugs specific to this gene signature across various datasets were identified using CMAP and the top hit ISOX chosen for further assessment. Considering the high mutational heterogeneity of PC, a five-cell line panel with diverse mutations was chosen for the validation studies. Cell line-based assessment for changes to proliferation using MTT and motility using matrigel assisted invasion showed the high potency of ISOX with an IC50 of 2.4nM-1.4µM & up to 90% reduction in invasion supporting initial hypothesis. It was further supported by the 48% induction of apoptosis. Molecular mechanism of ISOX was further assessed using an RNA-seq on ISOX treated PC cell lines followed by ingenuity pathway analyses. Gene ontology pathway analysis indicated SHH-WNT, PI3K-mTOR-AKT, and EGFR signaling as key pathways affected by ISOX. Further, 60% loss of viability was observed in pancreatic tumor organoids at 500nM of ISOX. Furthermore, about 10 fold statistically significant (p-value = 0.014) reduction in tumor weight at 50 mg/kg of ISOX both alone and in combination with 50 mg/kg 5FU (p-value=0.02) was observed in orthotopic mice models. Conclusion: Our data suggest that ISOX is a potential new therapeutic for PC. In future, we aim to evaluate the potency of newly identified drug in both transgenic mouse models of PC as well as clinical phase I studies. Citation Format: Pranita Atri, Parthasarathy Seshacharyulu, Satyanaryana Rachagani, Palanisamy Nallasamy, Sanchita Rauth, Garima Kaushik, Koelina Ganguly, Dario Ghersi, Moorthy P. Ponnusamy, Sukhwinder Kaur, Surinder K. Batra. Pre-clinical development of ISOX; A novel therapy for pancreatic cancer identified utilizing system biology approach(s) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4804.

The acute myeloid leukemia-associated Nucleophosmin 1 gene mutations dictate amyloidogenicity of the C-terminal domain.

Nucleophosmin 1 (NPM1) is a nucleus-cytoplasm shuttling protein ubiquitously expressed and highly conserved. It is involved in many cellular processes and its gene is mutated in ~50-60% of Acute Myeloid Leukemia (AML) patients. These mutations cause its cytoplasmic mislocation and accumulation (referred to as NPM1c+) and open the door to rational targeted therapy for AML diseases with mutated NPM1. Currently, there is limited knowledge on the mechanism of action of NPM1c+ and on structural determinants of the leukemogenic potential of AML mutations. Numerous previous studies outlined an unexpected amyloid-like aggregation tendency of several regions located in the C-terminal domain that, in wild-type form, fold as a three-helical-bundle. Here, using a combination of different techniques including Thioflavin T fluorescence, congo red absorbance, CD spectroscopy, Scanning Electron Microscopy (SEM) and wide-angle X-ray scattering on a series of peptides bearing mutations, we evidence that the amyloidogenicity of NPM1 mutants is directly linked to AML. Noticeably, AML point mutations strongly affect the amyloid cytotoxic effects in neuroblastoma cells and the morphologies of deriving fibrils. This study paves the way to deepen our understanding of AML-associated NPM1 mutants, and could help to break new ground for the identification of novel drugs targeting NPM1c+ for treatment of AML.

The importance of being parasiticidal… an update on drug development for the treatment of alveolar echinococcosis

The lethal disease alveolar echinococcosis (AE) is caused by the metacestode stage of the fox tapeworm Echinococcus multilocularis. Current chemotherapeutical treatment of AE relies on albendazole and mebendazole, with the caveat that these compounds are not parasiticidal. Drugs have to be taken for a prolonged period of time, often life-long, which can cause adverse effects and reduces the patients' quality of life. In some individuals, benzimidazoles are inactive or cause toxicity, leading to treatment discontinuation. Alternatives to benzimidazoles are urgently needed. Over the recent years, in vivo and in vitro models for low-to-medium throughput drug discovery against AE have been set in place. In vitro drug tests include the phosphoglucose-isomerase (PGI) assay to measure physical damage induced to metacestodes, and viability assays to assess parasiticidal activity against metacestodes and stem cells. In vitro models are also employed for studies on mechanisms of action. In vivo models are thus far based on rodents, mainly mice, and benefits could be gained in future by comparative approaches in naturally infected dogs or captive monkeys.For the identification of novel drugs against AE, a rare disease with a low expected market return, drug-repurposing is the most promising strategy. A variety of chemically synthesized compounds as well as natural products have been analyzed with respect to in vitro and/or in vivo activities against AE. We here review and discuss the most active of these compounds including anti-infective compounds (benzimidazoles, nitazoxanide, amphotericin B, itraconazole, clarithromycin, DB1127, and buparvaquone), the anti-infective anti-malarials (artemisinin, ozonids, mefloquine, and MMV665807) and anti-cancer drugs (isoflavones, 2-methoxyestradiol, methotrexate, navelbine, vincristine, kinase inhibitors, metallo-organic ruthenium complexes, bortezomib, and taxanes). Taking into account the efficacy as well as the potential availability for patients, the most promising candidates are new formulations of benzimidazoles and mefloquine. Future drug-repurposing approaches should also target the energy metabolism of E. multilocularis, in particular the understudied malate dismutation pathway, as this offers an essential target in the parasite, which is not present in mammals.

Non‑covalent proteasome inhibitor PI‑1840 induces apoptosis and autophagy in osteosarcoma cells.

Osteosarcoma (OS) is the predominant form of primary bone malignancy in children and adolescents. Although the combination of chemotherapy and modified surgical therapy leads to marked improvements in the survival rate, the therapeutic outcomes remain unsatisfactory. Therefore, the identification of novel drugs with higher efficacy and fewer side-effects is urgently required. Proteasome inhibitors have been approved by the Food and Drug Administration (FDA) for the treatment of certain cancers, although none of them are directed against OS. Non-covalent proteasome inhibitors, such as PI-1840, are superior to covalent ones in numerous respects in view of their chemical structure; however, to date, no studies have been published on the effects of non-covalent proteasome inhibitors on OS cells. In the present study, the antineoplastic effects of PI-1840 were systematically evaluated in the OS cell lines, MG-63 and U2-OS. Cell viability and morphological changes were assessed by Cell Counting Kit-8 (CCK-8) and live/dead assays. The cell cycle was analyzed using flow cytometry (FCM) and western blot analysis (assessing the levels of the proteins p21, p27, and the tyrosine kinase, WEE1). The extent of cell apoptosis and autophagy were assessed by FCM, western blot analysis [of the apoptosis-associated proteins, microtubule-associated protein 1 light chain 3 α (LC3) and Beclin1], and mRFP-GFP-LC3 adenovirus transfection assay. Transwell and wound healing assays, and western blot analysis of the matrix metalloproteinases (MMPs)2 and 9 were performed to preliminarily evaluate the migration and invasion capability of the cells. In the present study, our results revealed that PI-1840 inhibited the proliferation of OS cells and induced apoptosis, partly due to attenuation of the nuclear factor-κB (NF-κB) pathway. In addition, PI-1840-induced autophagy was detected, and inhibiting the autophagy of the OS cells led to an increase in the survival rate of the U2-OS cells rather than of the MG-63 cells. Furthermore, PI-1840 attenuated the migration and invasion capabilities of the OS cells. In conclusion, the present study revealed PI-1840 to be a promising drug for the treatment of OS.

Biosurfactant assisted silver nanoparticle synthesis: a critical analysis of its drug design aspects

Silver nanoparticles exhibit wide dimensional antimicrobial and antifungal properties and therefore they are preferred for their applications in medical and cosmetics sector. They are synthesized by physical, chemical and biochemical methods. Biochemical methods are the best technique because of their non-toxic and energy efficient nature and hence are alternatives for the production of silver nanoparticles. One of the biochemical methods proposed in this work is the use of microbial surfactant for the synthesis of silver nanoparticles. The synthesis of surfactant was performed using Bacillus subtilis. The surfactant thus produced was analyzed by emulsification assay, oil spilling test, and hemolytic test. Silver nanoparticles synthesized from surfactant were observed by UV–Vis spectroscopy in the range of 400–600 nm. The surface plasmon resonance peak was observed at 410 nm corresponding to the peak of silver nanoparticles. The x-ray diffraction pattern showed an intense diffraction peak at with an average particle size of 14 nm. In silico drug designing of surfactin A synthetase C was performed using Schrödinger software for identification of novel drugs. All the two identified ligands, leucine and lysine, exhibited docking with the target protein which shows their potential as drug molecules.

Melatonin activates FIS1, DYN1, and DYN2 Plasmodium falciparum related-genes for mitochondria fission: Mitoemerald-GFP as a tool to visualize mitochondria structure.

Malaria causes millions of deaths worldwide and is considered a huge burden to underdeveloped countries. The number of cases with resistance to all antimalarials is continuously increasing, making the identification of novel drugs a very urgent necessity. A potentially very interesting target for novel therapeutic intervention is the parasite mitochondrion. In this work, we studied in Plasmodium falciparum 3 genes coding for proteins homologues of the mammalian FIS1 (Mitochondrial Fission Protein 1) and DRP1 (Dynamin Related Protein 1) involved in mitochondrial fission. We studied the expression of P. falciparum genes that show ample sequence and structural homologies with the mammalian counterparts, namely FIS1, DYN1, and DYN2. The encoded proteins are characterized by a distinct pattern of expression throughout the erythrocytic cycle of P. falciparum, and their mRNAs are modulated by treating the parasite with the host hormone melatonin. We have previously reported that the knockout of the Plasmodium gene that codes for protein kinase 7 is essential for melatonin sensing. We here show that PfPk7 knockout results in major alterations of mitochondrial fission genes expression when compared to wild‐type parasites, and no change in fission proteins expression upon treatment with the host hormone. Finally, we have compared the morphological characteristics (using MitoTracker Red CMX Ros) and oxygen consumption properties of P. falciparum mitochondria in wild‐type parasites and PfPk7 Knockout strains. A novel GFP construct targeted to the mitochondrial matrix to wild‐type parasites was also developed to visualize P. falciparum mitochondria. We here show that, the functional characteristics of P. falciparum are profoundly altered in cells lacking protein kinase 7, suggesting that this enzyme plays a major role in the control of mitochondrial morphogenesis and maturation during the intra‐erythrocyte cell cycle progression.

Treatment-refractory giardiasis: challenges and solutions.

Giardia is the commonest parasitic diarrheal pathogen affecting humans and a frequent cause of waterborne/foodborne parasitic diseases worldwide. Prevalence of giardiasis is higher in children, living in poor, low hygiene settings in developing countries, and in travelers returning from highly endemic areas. The clinical picture of giardiasis is heterogeneous, with high variability in severity of clinical disease. It can become chronic or be followed by post-infectious sequelae. An alarming increase in cases refractory to the conventional treatment with nitroimidazoles (ie, metronidazole) has been reported in low prevalence settings, such as European Union countries, especially in patients returning from Asia. In view of its relevance, we aim in this review to recapitulate present clinical knowledge about Giardia, with a special focus on the challenge of treatment-refractory giardiasis. We propose a working definition of clinically drug-resistant giardiasis, summarize knowledge regarding resistance mechanisms, and discuss its clinical management according to research-based evidence and medical practice. Advances in development and identification of novel drugs and potential non-pharmacological alternatives are also reviewed with the overall aim to define knowledge gaps and suggest future directions for research.

DeepDTA: deep drug-target binding affinity prediction.

MotivationThe identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).ResultsThe results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.Availability and implementation https://github.com/hkmztrk/DeepDTA Supplementary information Supplementary data are available at Bioinformatics online.

9.1 GENOMICS AND PSYCHIATRIC DIAGNOSIS

BackgroundRecent genomic studies have begun to reveal the genetic architecture of psychiatric disorder and to give important insights into the relationship between the psychiatric syndromes that form the basis of current taxonomy. These studies have demonstrated the highly polygenic nature of psychiatric disorders, and have indicated that many individual genetic associations are shared across multiple disorders in a way that points to extensive biological pleiotropy and challenges the biological validity of existing diagnostic approaches.MethodsI will present genomic data, predominantly from the study of rare variants, that support the idea of a neurodevelopmental continuum, in which schizophrenia and bipolar disorder, together with childhood neurodevelopmental disorders, such as ID, ASD and ADHD represent the diverse range of outcomes that follow from disrupted or deviant brain development and furthermore that, within the neurodevelopmental continuum, severe mental illnesses occupy a gradient of decreasing neurodevelopmental impairment as follows: ID, ASD, schizophrenia and bipolar disorder. I will also present findings indicating that common genetic variation modifies the outcome of neurodevelopmental impairment explaining in part the diversity of psychiatric outcomes. Finally, I will explore how genetic data might be used to inform novel approaches to patient stratification which will be informative for prognosis and treatment response and facilitate the identification of novel drug targets.ResultsFinally, despite the undoubted complexity and the fact that much of the genetic risk remains unaccounted for at the DNA level, there are encouraging signs that the genes implicated in schizophrenia converge onto sets of plausible biological processes. In particular, the data point to synaptic function and histone modification and implicate mechanisms involved in brain plasticity that are important in development and in learning and cognition. While these are almost certainly not the only processes involved, they provide robust entry points for clinical and basic neuroscience research.DiscussionN/A

Venomics-Accelerated Cone Snail Venom Peptide Discovery.

Cone snail venoms are considered a treasure trove of bioactive peptides. Despite over 800 species of cone snails being known, each producing over 1000 venom peptides, only about 150 unique venom peptides are structurally and functionally characterized. To overcome the limitations of the traditional low-throughput bio-discovery approaches, multi-omics systems approaches have been introduced to accelerate venom peptide discovery and characterisation. This “venomic” approach is starting to unravel the full complexity of cone snail venoms and to provide new insights into their biology and evolution. The main challenge for venomics is the effective integration of transcriptomics, proteomics, and pharmacological data and the efficient analysis of big datasets. Novel database search tools and visualisation techniques are now being introduced that facilitate data exploration, with ongoing advances in related omics fields being expected to further enhance venomics studies. Despite these challenges and future opportunities, cone snail venomics has already exponentially expanded the number of novel venom peptide sequences identified from the species investigated, although most novel conotoxins remain to be pharmacologically characterised. Therefore, efficient high-throughput peptide production systems and/or banks of miniaturized discovery assays are required to overcome this bottleneck and thus enhance cone snail venom bioprospecting and accelerate the identification of novel drug leads.