Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival

<p>Abstract</p> <p>Background</p> <p>We have engaged in an international program designated the <it>Bank On A Cure</it>, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma.</p> <p>We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, <it>n </it>= 70) versus long term progression-free survivors (greater than 3 years, <it>n </it>= 73) in two phase III clinical trials.</p> <p>Results</p> <p>Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.</p> <p>Conclusion</p> <p>A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.</p

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.Medicinal Chemistr

Computational Analysis of Structure-Activity Relationships : From Prediction to Visualization Methods

Understanding how structural modifications affect the biological activity of small molecules is one of the central themes in medicinal chemistry. By no means is structure-activity relationship (SAR) analysis a priori dependent on computational methods. However, as molecular data sets grow in size, we quickly approach our limits to access and compare structures and associated biological properties so that computational data processing and analysis often become essential. Here, different types of approaches of varying complexity for the analysis of SAR information are presented, which can be applied in the context of screening and chemical optimization projects. The first part of this thesis is dedicated to machine-learning strategies that aim at de novo ligand prediction and the preferential detection of potent hits in virtual screening. High emphasis is put on benchmarking of different strategies and a thorough evaluation of their utility in practical applications. However, an often claimed disadvantage of these prediction methods is their "black box" character because they do not necessarily reveal which structural features are associated with biological activity. Therefore, these methods are complemented by more descriptive SAR analysis approaches showing a higher degree of interpretability. Concepts from information theory are adapted to identify activity-relevant structure-derived descriptors. Furthermore, compound data mining methods exploring prespecified properties of available bioactive compounds on a large scale are designed to systematically relate molecular transformations to activity changes. Finally, these approaches are complemented by graphical methods that primarily help to access and visualize SAR data in congeneric series of compounds and allow the formulation of intuitive SAR rules applicable to the design of new compounds. The compendium of SAR analysis tools introduced in this thesis investigates SARs from different perspectives

Investigating The Grey Field Slug

High-throughput sequencing was used to analyse cDNA generated from tissues of the grey field slug, Deroceras reticulatum, a significant invertebrate pest of agricultural and horticultural crops. Almost no sequence data is available for this organism. In this project, we performed de novo transcriptome sequencing to produce sequence dataset for the Deroceras reticulatum. A total of 132,597 and 161,419 sequencing reads between 50-600bp from the digestive gland and neural tissue were obtained through Roche 454 pyrosequencing. These reads were assembled into contiguous sequences and annotated using sequence homology search tools. Multiple sequence assemblies and annotation data was amalgamated into a biological database using BioSQL. Analysis of the dataset with predictions of probable protein function were made based on annotation data. InterPro (IPR) terms generated with InterProScan software were mapped to read counts and used to identify more frequently sequenced gene families. Digestive hydrolases were major transcripts in the digestive gland, with cysteine proteinases and cellulases being the most abundant functional classes. A Cathepsin L homologue is likely to be responsible for the proteinase activity of the digestive gland which was previously detected by biochemical analysis. Cathepsin L and several other predicted proteins were used to design RNAi experiments to assess potential for crop pest defence strategy. Further work on protein expression of a native tumour necrosis factor (TNF) ligand homologue was also conducted as an exemplar study

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor

Systems Modeling of Collagenous Tissue Degradation: Protease and Mechanical Interactions

Heart failure (HF) is a chronic, progressive condition defined as an abnormality of cardiac function with the inability of the heart muscle to pump enough blood to meet the body’s requirements for metabolism. HF has various contributing pathologies, including hypertension (86 million Americans), myocardial infarction (MI, 800,000 Americans per year and 300,000 recurrent infarctions each year), both of which promote fibrosis. Myocardial fibrosis contributes to left ventricular (LV) dysfunction and is histologically defined by excessive deposition of fibrous tissue relative to the mass of cardiomyocytes within the myocardial tissue. Quantitatively, myocardial fibrosis is characterized by increased collagen volume fraction (CVF) or percentage of myocardial tissue with collagen fibers. Currently, there are no prescribed therapeutics for preventing cardiac fibrosis, and clinicians are unable to predict which patients at what time and to what extent are more likely to develop fibrosis. Collagen accumulation contributes to increased stiffness and loss of function in failing hearts, and cardiac fibrosis remains a significant barrier to the treatment and prevention of HF. Collagen remodeling is regulated by a complex network of extracellular interactions, including: (1) collagen secretion, (2) protease secretion, activation, and degradation of collagen (namely Matrix Metalloproteinase (MMP) and Cathepsins), and (3) tissue inhibitors of metalloproteinases (TIMP) secretion and inhibition of MMPs. Importantly, this network is sensitive to mechanical tension. Fibroblast expression of collagen, MMPs, and TIMPs all depend on tension, and it is known that an excessive amount of tension can damage matrix fibers. There is also evidence that protease degradation of collagen can depend on fiber tension. However, it is unknown how tension affects collagen degradation by different proteases and protease mixes. The overarching objective of this dissertation is to develop a computational model of collagen turnover under combinatory chemo-mechano-conditions as a predictive tool for stratifying fibrotic risk for HF patients. Firstly, we tested the effect of tensile loading on collagenous tissue degradation by proteases. We picked four proteases and quantified the role of mechanical loading on the degradation of collagenous tissue by each protease. As matrix degradation leads to decaying force levels, sample degradation rate was quantified for different strain levels for each protease. Secondly, we developed a detailed biochemical network computational model of collagen I proteolysis capturing all interactions of type I collagen, four MMPs, and three TIMPs in a cell-free, well-stirred environment. We monitored the proteolytic activity of MMPs and inhibitory activity of TIMPs and then used the results from experimental data to fit five different hypothetical reaction topologies and determined kinetic rate constants for collagen degradation by MMPs, MMP inhibition by TIMPs, MMP and TIMP inactivation, MMP cannibalism, and MMP and TIMP distraction. We also used post-MI time courses of collagen, MMP, and TIMP levels in animal experiments from the literature to perform a parameter sensitivity analysis across the model reaction rates to identify which molecules or interactions are the essential regulators of ECM post-MI for both early and late time-periods. Lastly, we developed an ensemble classification algorithm for diagnosing HF patients with preserved ejection fraction (HFpEF) within a population of 459 individuals, including HFpEF patients and referent control patients. We concluded that machine learning algorithms could substantially improve the predictive value of circulating plasma biomarkers. Additionally, we built a mechanistic model to predict ECM component degradation using a genetic algorithm to connect ECM remodeling to the plasma biomarkers to help us with HFpEF patients’ classification. Our findings demonstrate that machine learning-based classification algorithms show promise as a non-invasive diagnostic tool for HFpEF patients’ classification while also suggesting priority biomarkers for future mechanistic studies to elucidate more specific regulatory roles. Our work suggests that computational modeling can serve as a beneficial tool for HF prognosis and potentially developing novel therapeutics

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Scope of 3D shape-based approaches in predicting the macromolecular targets of structurally complex small molecules including natural products and macrocyclic ligands

A plethora of similarity-based, network-based, machine learning, docking and hybrid approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing guidance in early drug discovery. With the increasing maturity of target prediction methods, researchers have started to explore ways to expand their scope to more challenging molecules such as structurally complex natural products and macrocyclic small molecules. In this work, we systematically explore the capacity of an alignment-based approach to identify the targets of structurally complex small molecules (including large and flexible natural products and macrocyclic compounds) based on the similarity of their 3D molecular shape to noncomplex molecules (i.e., more conventional, “drug-like”, synthetic compounds). For this analysis, query sets of 10 representative, structurally complex molecules were compiled for each of the 28 pharmaceutically relevant proteins. Subsequently, ROCS, a leading shape-based screening engine, was utilized to generate rank-ordered lists of the potential targets of the 28 × 10 queries according to the similarity of their 3D molecular shapes with those of compounds from a knowledge base of 272 640 noncomplex small molecules active on a total of 3642 different proteins. Four of the scores implemented in ROCS were explored for target ranking, with the TanimotoCombo score consistently outperforming all others. The score successfully recovered the targets of 30% and 41% of the 280 queries among the top-5 and top-20 positions, respectively. For 24 out of the 28 investigated targets (86%), the method correctly assigned the first rank (out of 3642) to the target of interest for at least one of the 10 queries. The shape-based target prediction approach showed remarkable robustness, with good success rates obtained even for compounds that are clearly distinct from any of the ligands present in the knowledge base. However, complex natural products and macrocyclic compounds proved to be challenging even with this approach, although cases of complete failure were recorded only for a small number of targets.publishedVersio

Insights into pulmonary phosphate homeostasis and osteoclastogenesis emerge from the study of pulmonary alveolar microlithiasis

Pulmonary alveolar microlithiasis is an autosomal recessive lung disease caused by a deficiency in the pulmonary epithelial Npt2b sodium-phosphate co-transporter that results in accumulation of phosphate and formation of hydroxyapatite microliths in the alveolar space. The single cell transcriptomic analysis of a pulmonary alveolar microlithiasis lung explant showing a robust osteoclast gene signature in alveolar monocytes and the finding that calcium phosphate microliths contain a rich protein and lipid matrix that includes bone resorbing osteoclast enzymes and other proteins suggested a role for osteoclast-like cells in the host response to microliths. While investigating the mechanisms of microlith clearance, we found that Npt2b modulates pulmonary phosphate homeostasis through effects on alternative phosphate transporter activity and alveolar osteoprotegerin, and that microliths induce osteoclast formation and activation in a receptor activator of nuclear factor-κB ligand and dietary phosphate dependent manner. This work reveals that Npt2b and pulmonary osteoclast-like cells play key roles in pulmonary homeostasis and suggest potential new therapeutic targets for the treatment of lung disease