Discovery Pipeline Research Articles

FAIR data management: what does it mean for drug discovery?

The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process.

Development of a new drug candidate for the inhibition of Lassa virus glycoprotein and nucleoprotein by modification of evodiamine as promising therapeutic agents.

The Lassa virus (LASV), an RNA virus prevalent in West and Central Africa, causes severe hemorrhagic fever with a high fatality rate. However, no FDA-approved treatments or vaccines exist. Two crucial proteins, LASV glycoprotein and nucleoprotein, play vital roles in pathogenesis and are potential therapeutic targets. As effective treatments for many emerging infections remain elusive, cutting-edge drug development approaches are essential, such as identifying molecular targets, screening lead molecules, and repurposing existing drugs. Bioinformatics and computational biology expedite drug discovery pipelines, using data science to identify targets, predict structures, and model interactions. These techniques also facilitate screening leads with optimal drug-like properties, reducing time, cost, and complexities associated with traditional drug development. Researchers have employed advanced computational drug design methods such as molecular docking, pharmacokinetics, drug-likeness, and molecular dynamics simulation to investigate evodiamine derivatives as potential LASV inhibitors. The results revealed remarkable binding affinities, with many outperforming standard compounds. Additionally, molecular active simulation data suggest stability when bound to target receptors. These promising findings indicate that evodiamine derivatives may offer superior pharmacokinetics and drug-likeness properties, serving as a valuable resource for professionals developing synthetic drugs to combat the Lassa virus.

A biomarker assay validation approach tailored to thecontext of use and bioanalytical platform.

It is widely acknowledged by the bioanalytical and biomarker community that biomarker assay validations should be fit-for-purpose depending on the context of use. The challenge is how to consistently apply these principles in teams responsible for measuring a disparate array of biomarkers, often on multiple analytical platforms, at various stages of the drug discovery and development pipeline and across diverse biology focus areas. To drive consistency, while maintaining the necessary flexibility to allow validations to be driven by scientific rationale and taking into consideration the context of use and associated biological and (pre)analytical factors, a framework applicable across biomarker assayswas developed. Herein the authorsshare their perspective to engage in the ongoing conversation around fit-for-purpose biomarker assay validation.

A target discovery pipeline identified ILT3 as a target for immunotherapy of multiple myeloma

Multiple myeloma (MM) is an incurable malignancy of plasma cells. To identify targets for MM immunotherapy, we develop an integrated pipeline based on mass spectrometry analysis of seven MM cell lines and RNA sequencing (RNA-seq) from 900+ patients. Starting from 4,000+ candidates, we identify the most highly expressed cell surface proteins. We annotate candidate protein expression in many healthy tissues and validate the expression of promising targets in 30+ patient samples with relapsed/refractory MM, as well as in primary healthy hematopoietic stem cells and Tcells by flow cytometry. Six candidates (ILT3, SEMA4A, CCR1, LRRC8D, FCRL3, IL12RB1) and B cell maturation antigen (BCMA) present the most favorable profile in malignant and healthy cells. We develop a bispecific Tcell engager targeting ILT3 that shows potent killing effects invitro and decreased tumor burden and prolonged mice survival invivo, suggesting therapeutic relevance. Our study uncovers MM-associated antigens that hold great promise for immune-based therapies of MM.

Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis

Major advances in RNA secondary structural motif prediction have been achieved in the last few years; however, few methods harness the predictive power of multiple approaches to deliver in-depth characterizations of local RNA motifs and their potential functionality. Additionally, most available methods do not predict RNA pseudoknots. This work combines complementary bioinformatic systems into one robust discovery pipeline where:•RNA sequences are folded to search for thermodynamically favorable motifs utilizing ScanFold.•Motifs are expanded and refolded into alternate pseudoknot conformations by Knotty/Iterative HFold.•All conformations are evaluated for covariance via the cm-builder pipeline (Infernal and R-scape).

DprE2 is a molecular target of the anti-tubercular nitroimidazole compounds pretomanid and delamanid

Mycobacterium tuberculosis is one of the global leading causes of death due to a single infectious agent. Pretomanid and delamanid are new antitubercular agents that have progressed through the drug discovery pipeline. These compounds are bicyclic nitroimidazoles that act as pro-drugs, requiring activation by a mycobacterial enzyme; however, the precise mechanisms of action of the active metabolite(s) are unclear. Here, we identify a molecular target of activated pretomanid and delamanid: the DprE2 subunit of decaprenylphosphoribose-2’-epimerase, an enzyme required for the synthesis of cell wall arabinogalactan. We also provide evidence for an NAD-adduct as the active metabolite of pretomanid. Our results highlight DprE2 as a potential antimycobacterial target and provide a foundation for future exploration into the active metabolites and clinical development of pretomanid and delamanid.

Early Prediction of Ion Transport Properties in Solid Polymer Electrolytes Using Machine Learning and System Behavior-Based Descriptors of Molecular Dynamics Simulations

Molecular dynamics simulations are useful tools to screen solid polymer electrolytes with suitable properties applicable to Li-ion batteries. However, due to the vast design space of polymers, it is highly desirable to accelerate the screening by reducing the computational time of ion transport properties from simulations. In this study, we show that with a judicious choice of descriptors we can predict the equilibrium ion transport properties in LiTFSI–homopolymer systems within the first 0.5 ns of the production run of simulations. Specifically, we find that descriptors that include information about the behavior of the system, such as ion clustering and time evolution of ion transport properties, have several advantages over polymer structure-based descriptors, as they encode system (polymer and salt) behavior rather than just the class of polymers and can be computed at any time point during the simulations. These characteristics increase the applicability of our descriptors to a wide range of polymer systems (e.g., copolymers, blend of polymers, salt concentrations, and temperatures) and can be impactful in significantly shortening the discovery pipeline for solid polymer electrolytes.

A Call for Discovery and Therapeutic Development for Cutaneous Neurofibromas

A Call for Discovery and Therapeutic Development for Cutaneous Neurofibromas

Protein signatures of spontaneous lipolysis and lipoprotein lipase activity in cow's milk

Spontaneous milk lipolysis refers to the breakdown of triacylglycerols in milk. Lipolysis impacts the organoleptic value of milk by causing off-flavours and reduces the technological properties of milk. Lipolysis is caused by lipoprotein lipase (LPL), a tightly regulated enzyme in milk. Our objective was to identify robust biomarkers of lipolysis and putative regulators of LPL enzyme in bovine milk. To achieve this goal, we used feed restriction as a lever to generate highly contrasted samples with regard to milk lipolysis. We combined statistical methods on proteomics data, milk lipolysis and LPL activity values. Following this strategy, we identified CD5L and GP2 as robust biomarkers of high lipolysis in cow milk. We also identified HID1, SURF4 and CUL9 as putative inhibitors of the lipolytic process in the milk. We thus proposed 5 putative biomarkers to be considered in future tools to manage milk lipolysis. SignificanceThis manuscript is notable in three aspects. First, this is the first evaluation of the milk proteome relative to milk lipolysis or LPL activity. Second, the relationship between the abundance of proteins and milk traits was evaluated by a combination of univariate and multivariate analyses. Third, we provide a short list of five proteins to be tested in a larger population to feed the pipeline of biomarker discovery.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases.

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

MDB-45. SELECTIVE B7-H3 INHIBITORS NI1 AND NI4 REDUCE AGGRESSIVENESS OF GROUP 3 MEDULLOBLASTOMA

Abstract Medulloblastoma (MB) is a primary childhood malignancy of the central nervous system (CNS). Clinically heterogenous MB are sub-grouped into four molecular subgroups, i.e. Wingless (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4. Among all, Group 3 MB (G3MB) is known to be the most aggressive form, with less than 50% five-year survival. G3MB are associated with large-cell/anaplastic (LCA) histology, distinctive methylation profile, amplification of MYC, and isochromosome 17q (i17q). Targeting these high-risk molecular features unique to G3MB has been challenging, necessitating alternative approaches to therapy for G3-MB. One innovative approach is immunotherapy, which is largely impeded by inhibitory immune checkpoints. B7 homolog 3 (B7-H3/CD276), an immunosuppressive immune checkpoint protein and member of B7 superfamily, is significantly enriched in pediatric malignancies of CNS including G3MB. The enhanced expression of B7-H3/CD276 has been associated with aggressive and metastatic potential. B7-H3/CD276 is also a primary target of miR-1253, a novel tumor suppressor gene silenced in G3MB. Using a structure-based drug discovery pipeline, we screened the ChemBridge library of compounds to discover selective B7-H3 small-molecule inhibitors. Selective B7-H3 inhibitors Ni1 and Ni4 exhibited strong binding scores, CNS penetrance, and oral bioavailability. In vitro, both compounds had appreciably low IC50 values of 3µM (Ni1) and 0.75µM (Ni4). Further, both compounds inhibited wound healing and colony formation. Ni1 shifted the thermostability of B7-H3, suggesting in vitro biding. Ni1 has also undergone PK/PD and MTD studies in vivo, showing stability in serum, good blood-brain barrier permeability, and a half-life of 8 hours. We are now elucidating B7-H3-activated downstream signaling through JAK2/STAT3 signaling and testing the in vivo efficacy for Ni1 against G3MB tumors in a murine model.

Recent Developments in Mass Spectrometry to Support Next-Generation Synthesis and Screening.

The complexity of new therapeutics continues to increase and the timeline for the discovery of these therapeutics continues to shrink. This creates demand for new analytical techniques to facilitate quicker discovery and development of novel drugs. Mass spectrometry is one of the most prolific analytical techniques that has been applied across the entire drug discovery pipeline. New mass spectrometers and the associated methods for sampling have been introduced at a rate that keeps pace with new chemistries, therapeutic types, and screening practices used by modern drug hunters. This microperspective covers application and implementation of new mass spectrometry workflows that enable current and future efforts in screening and synthesis for drug discovery.

Going through phages: a computational approach to revealing the role of prophage in Staphylococcus aureus.

Prophages have important roles in virulence, antibiotic resistance, and genome evolution in Staphylococcus aureus . Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences at an unprecedented scale. We developed a novel computational pipeline for phage discovery and annotation. We combined PhiSpy, a phage discovery tool, with VGAS and PROKKA, genome annotation tools to detect and analyse prophage sequences in nearly 10 011 S . aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance. To our knowledge, this is the first large-scale application of PhiSpy on a large-scale set of genomes (10 011 S . aureus ). Determining the presence of virulence and resistance encoding genes in prophage has implications for the potential transfer of these genes/functions to other bacteria via transduction and thus can provide insight into the evolution and spread of these genes/functions between bacterial strains. While the phage we have identified may be known, these phages were not necessarily known or characterized in S. aureus and the clustering and comparison we did for phage based on their gene content is novel. Moreover, the reporting of these genes with the S. aureus genomes is novel.

Nucleophilic covalent ligand discovery for the cysteine redoxome.

With an eye toward expanding chemistries used for covalent ligand discovery, we elaborated an umpolung strategy that exploits the 'polarity reversal' of sulfur when cysteine is oxidized to sulfenic acid, a widespread post-translational modification, for selective bioconjugation with C-nucleophiles. Here we present a global map of a human sulfenome that is susceptible to covalent modification by members of a nucleophilic fragment library. More than 500 liganded sulfenic acids were identified on proteins across diverse functional classes, and, of these, more than 80% were not targeted by electrophilic fragment analogs. We further show that members of our nucleophilic fragment library can impair functional protein-protein interactions involved in nuclear oncoprotein transport and DNA damage repair. Our findings reveal a vast expanse of ligandable sulfenic acids in the human proteome and highlight the utility of nucleophilic small molecules in the fragment-based covalent ligand discovery pipeline, presaging further opportunities using non-traditional chemistries for targeting proteins.

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals to the classical ones, and a hybrid solution is the recommended approach at present.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis.

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

C-1 Substituted isoquinolines potentiate the antimycobacterial activity of rifampicin and ethambutol

IntroductionThe emergence of extensively drug-resistant strains ofMycobacterium tuberculosisthreatens decades of progress in the treatment of a disease which remains one of the leading infectious causes of death worldwide. The development of novel antimycobacterial compounds is therefore essential to reinforce the existing antitubercular drug discovery pipeline. There is also interest in new compounds which can synergize with existing antitubercular drugs and can be deployed as part of a combination therapy. This strategy could serve to delay the emergence of resistance to first-line anti-tuberculosis drugs and increase their efficacy against resistant strains of tuberculosis. Previous research has established that several C-1 substituted tetrahydroisoquinolines have antimycobacterial activity. Here we sought to expand our understanding of their antimycobacterial structure activity relationships and their potential to act as adjunct therapies alongside existing antitubercular drugs.MethodsThree chemical series were synthesised and assayed for their antimycobacterial potency, mammalian cell toxicity, inhibition of whole-cell efflux and synergism with isoniazid, rifampicin, and ethambutol.ResultsSeveral compounds were found to inhibit the growth of mycobacteria. Potent inhibitors of whole-cell efflux were also identified, as well as compounds which exhibited synergism with rifampicin and ethambutol.ConclusionsStructure-activity relationships were identified for antimycobacterial potency, improved selectivity, whole cell efflux inhibition and synergism. Potent whole-cell efflux inhibitors and synergistic compounds were identified, suggesting potential development as adjuncts to existing anti-tuberculosis chemotherapy.

Fragment Linker Prediction Using the Deep Encoder-Decoder Network for PROTACs Drug Design.

A drug discovery and development pipeline is a prolonged and complex process that remains challenging for both computational methods and medicinal chemists and has not been able to be resolved using computational methods. Deep learning has been utilized in various fields and achieved tremendous success in designing novel molecules in the pharmaceutical industry. Herein, we use state-of-the-art techniques to propose a deep neural network, AIMLinker, to rapidly design and generate meaningful drug-like proteolysis targeting chimeras (PROTACs) analogs. The model extracts the structural information from the input fragments and generates linkers to incorporate them. We integrate filters in the model to exclude nondruggable structures guided via protein-protein complexes while retaining molecules with potent chemical properties. The novel PROTACs subsequently pass through molecular docking, taking root-mean-square deviation (RMSD), relative Gibbs free energy (ΔΔGbinding), molecular dynamics (MD) simulation, and free energy perturbation (FEP) calculations as the measurement criteria for testing the robustness and feasibility of the model. The generated novel PROTACs molecules possess similar structural information with superior binding affinity to the binding pockets compared to the existing CRBN-dBET6-BRD4 ternary complexes. We demonstrate the effectiveness of the methodology of leveraging AIMLinker to design novel compounds for PROTACs molecules exhibiting better chemical properties compared to the dBET6 crystal pose.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Fungal plant pathogen "mutagenomics" reveals tagged and untagged mutations in Zymoseptoria tritici and identifies SSK2 as key morphogenesis and stress-responsive virulence factor.

"Mutagenomics" is the combination of random mutagenesis, phenotypic screening, and whole-genome re-sequencing to uncover all tagged and untagged mutations linked with phenotypic changes in an organism. In this study, we performed a mutagenomics screen on the wheat pathogenic fungus Zymoseptoria tritici for altered morphogenetic switching and stress sensitivity phenotypes using Agrobacterium-mediated "random" T-DNA mutagenesis (ATMT). Biological screening identified four mutants which were strongly reduced in virulence on wheat. Whole genome re-sequencing defined the positions of the T-DNA insertion events and revealed several unlinked mutations potentially affecting gene functions. Remarkably, two independent reduced virulence mutant strains, with similarly altered stress sensitivities and aberrant hyphal growth phenotypes, were found to have a distinct loss of function mutations in the ZtSSK2 MAPKKK gene. One mutant strain had a direct T-DNA insertion affecting the predicted protein's N-terminus, while the other possessed an unlinked frameshift mutation towards the C-terminus. We used genetic complementation to restore both strains' wild-type (WT) function (virulence, morphogenesis, and stress response). We demonstrated that ZtSSK2 has a non-redundant function with ZtSTE11 in virulence through the biochemical activation of the stress-activated HOG1 MAPK pathway. Moreover, we present data suggesting that SSK2 has a unique role in activating this pathway in response to specific stresses. Finally, dual RNAseq-based transcriptome profiling of WT and SSK2 mutant strains revealed many HOG1-dependent transcriptional changes in the fungus during early infection and suggested that the host response does not discriminate between WT and mutant strains during this early phase. Together these data define new genes implicated in the virulence of the pathogen and emphasise the importance of a whole genome sequencing step in mutagenomic discovery pipelines.