Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Discovery of 1(2H)-phthalazinone and 1(2H)-isoquinolinone derivatives as potent hematopoietic progenitor kinase 1 (HPK1) inhibitors

Although immune checkpoint inhibitors (ICIs) have been a revelation for treating several cancers, an unmet need remains to broaden ICI therapeutic scope and increase their response rates in clinical trials. Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T cell activation and has previously been identified as a promising target for immunotherapy. Herein, we report the discovery of a series of HPK1 inhibitors with novel 1(2H)-phthalazinone and 1(2H)-isoquinolinone scaffolds. Among them, compound 24 demonstrated potent in vitro activity (HPK1 IC50 value of 10.4 nM) and cellular activity (pSLP76 EC50 = 41 nM & IL-2 EC50 = 108 nM). Compound 24 exhibited favorable mouse and rat pharmacokinetic profiles with reasonable oral exposure. Compound 24 showed potent in vivo anti-tumor activity in a CT26 syngeneic tumor model with 95 % tumor growth inhibition in combination with anti-PD-1.

Accelerating drug discovery, development, and clinical trials by artificial intelligence

Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assisted drug development, particularly focusing on small molecules, RNA, and antibodies. Moreover, this paper elucidates the current integration of AI methodologies within the industrial drug development framework. This encompasses a detailed examination of the industry-standard drug development process, supplemented by a review of medications presently undergoing clinical trials. Conclusively, the paper tackles a predominant obstacle within the AI pharmaceutical sector: the absence of AI-conceived drugs receiving approval. This paper also advocates for the adoption of large language models and diffusion models as a viable strategy to surmount this challenge. This review not only underscores the significant potential of AI in drug discovery but also deliberates on the challenges and prospects within this dynamically progressing field.

Emerging pharmacological options in the treatment of idiopathic pulmonary fibrosis (IPF)

INTRODUCTION

Idiopathic pulmonary fibrosis (IPF) is a progressive-fibrosing lung disease with a median survival of less than 5 years. Currently, two agents, pirfenidone and nintedanib are approved for this disease, and both have been shown to reduce the rate of decline in lung function in patients with IPF. However, both have significant adverse effects and neither completely arrest the decline in lung function.

AREAS COVERED

Thirty experimental agents with unique mechanisms of action that are being evaluated for the treatment of IPF are discussed. These agents work through various mechanisms of action, these include inhibition of transcription nuclear factor k-B on fibroblasts, reduced expression of metalloproteinase 7, the generation of more lysophosphatidic acids, blocking the effects of transforming growth factor ß, and reducing reactive oxygen species as examples of some unique mechanisms of action of these agents.

EXPERT OPINION

New drug development has the potential to expand the treatment options available in the treatment of IPF patients. It is expected that the adverse drug effect profiles will be more favorable than current agents. It is further anticipated that these new agents or combinations of agents will arrest the fibrosis, not just slow the fibrotic process.

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

The role of lncRNAs related ceRNA regulatory network in multiple hippocampal pathological processes during the development of perioperative neurocognitive disorders

Background

Perioperative neurocognitive disorders (PND) refer to neurocognitive abnormalities during perioperative period, which are a great challenge for elderly patients and associated with increased morbidity and mortality. Our studies showed that long non-coding RNAs (lncRNAs) regulate mitochondrial function and aging-related pathologies in the aged hippocampus after anesthesia, and lncRNAs are associated with multiple neurodegenerations. However, the regulatory role of lncRNAs in PND-related pathological processes remains unclear.

Methods

A total of 18-month mice were assigned to control and surgery (PND) groups, mice in PND group received sevoflurane anesthesia and laparotomy. Cognitive function was assessed with fear conditioning test. Hippocampal RNAs were isolated for sequencing, lncRNA and microRNA libraries were constructed, mRNAs were identified, Gene Ontology (GO) analysis were performed, and lncRNA-microRNA-mRNA networks were established. qPCR was performed for gene expression verification.

Results

A total of 312 differentially expressed (DE) lncRNAs, 340 DE-Transcripts of Uncertain Coding Potential (TUCPs), and 2,003 DEmRNAs were identified in the hippocampus between groups. The lncRNA-microRNA-mRNA competing endogenous RNA (ceRNA) network was constructed with 29 DElncRNAs, 90 microRNAs, 493 DEmRNAs, 148 lncRNA-microRNA interaction pairs, 794 microRNA-mRNA interaction pairs, and 110 lncRNA-mRNA co-expression pairs. 795 GO terms were obtained. Based on the frequencies of involved pathological processes, BP terms were divided into eight categories: neurological system alternation, neuronal development, metabolism alternation, immunity and neuroinflammation, apoptosis and autophagy, cellular communication, molecular modification, and behavior changes. LncRNA-microRNA-mRNA ceRNA networks in these pathological categories were constructed, and involved pathways and targeted genes were revealed. The top relevant lncRNAs in these ceRNA networks included RP23-65G6.4, RP24-396L14.1, RP23-251I16.2, XLOC_113622, RP24-496E14.1, etc., and the top relevant mRNAs in these ceRNA networks included Dlg4 (synaptic function), Avp (lipophagy), Islr2 (synaptic function), Hcrt (regulation of awake behavior), Tnc (neurotransmitter uptake).

Conclusion

In summary, we have constructed the lncRNA-associated ceRNA network during PND development in mice, explored the role of lncRNAs in multiple pathological processes in the mouse hippocampus, and provided insights into the potential mechanisms and therapeutic gene targets for PND.

Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development

Despite advances in artificial intelligence (AI), target-based drug development remains a costly, complex and imprecise process. We introduce F.O.R.W.A.R.D [ Framework for Outcome-based Research and Drug Development ], a network-based target prioritization approach and test its utility in the challenging therapeutic area of Inflammatory Bowel Diseases (IBD), which is a chronic condition of multifactorial origin. F.O.R.W.A.R.D leverages real-world outcomes, using a machine-learning classifier trained on transcriptomic data from seven prospective randomized clinical trials involving four drugs. It establishes a molecular signature of remission as the therapeutic goal and computes, by integrating principles of network connectivity, the likelihood that a drug's action on its target(s) will induce the remission-associated genes. Benchmarking F.O.R.W.A.R.D against 210 completed clinical trials on 52 targets showed a perfect predictive accuracy of 100%. The success of F.O.R.W.A.R.D was achieved despite differences in targets, mechanisms, and trial designs. F.O.R.W.A.R.D-driven in-silico phase '0' trials revealed its potential to inform trial design, justify re-trialing failed drugs, and guide early terminations. With its extendable applications to other therapeutic areas and its iterative refinement with emerging trials, F.O.R.W.A.R.D holds the promise to transform drug discovery by generating foresight from hindsight and impacting research and development as well as human-in-the-loop clinical decision-making.

Exploring the role of candidalysin in the pathogenicity of Candida albicans by gene set enrichment analysis and evolutionary dynamics

AIMS

To explore the pathogenic mechanisms of Candida albicans (C. albicans), focusing on its impact on human health, particularly through invasive infections in the gastrointestinal and respiratory tracts.

METHODS

In this study, we evaluated the demographic and clinical profiles of 7 pneumonia patients. Meanwhile, we used Gene Set Enrichment Analysis (GSEA) and Evolutionary Dynamics method to analyze the role of candidalysin in C. albicans pathogenicity.

RESULTS

By analyzing genomic data and conducting biomedical text mining, we identified novel mutation sites in the candidalysin coding gene ECE1-III, shedding light into the genetic diversity within C. albicans strains and their potential implications for antifungal resistance. Our results revealed significant associations between C. albicans and respiratory as well as gastrointestinal diseases, emphasizing the fungus's role in the pathogenesis of these diseases. Additionally, we identified a new mutation site in the C. albicans strain YF2-5, isolated from patients with pneumonia. This mutation may be associated with its heightened pathogenicity.

CONCLUSION

Our research advances the understanding of C. albicans pathogenicity and opens new avenues for developing targeted antifungal therapies. By focusing on the molecular basis of fungal virulence, we aim to contribute to the development of more effective treatment strategies, addressing the challenge of multidrug resistance in invasive fungal infections.

Downregulation of the silent potassium channel Kv8.1 increases motor neuron vulnerability in amyotrophic lateral sclerosis

While voltage-gated potassium channels have critical roles in controlling neuronal excitability, they also have non-ion-conducting functions. Kv8.1, encoded by the KCNV1 gene, is a 'silent' ion channel subunit whose biological role is complex since Kv8.1 subunits do not form functional homotetramers but assemble with Kv2 to modify its ion channel properties. We profiled changes in ion channel expression in amyotrophic lateral sclerosis patient-derived motor neurons carrying a superoxide dismutase 1(A4V) mutation to identify what drives their hyperexcitability. A major change identified was a substantial reduction of KCNV1/Kv8.1 expression, which was also observed in patient-derived neurons with C9orf72 expansion. We then studied the effect of reducing KCNV1/Kv8.1 expression in healthy motor neurons and found it did not change neuronal firing but increased vulnerability to cell death. A transcriptomic analysis revealed dysregulated metabolism and lipid/protein transport pathways in KCNV1/Kv8.1-deficient motor neurons. The increased neuronal vulnerability produced by the loss of KCNV1/Kv8.1 was rescued by knocking down Kv2.2, suggesting a potential Kv2.2-dependent downstream mechanism in cell death. Our study reveals, therefore, unsuspected and distinct roles of Kv8.1 and Kv2.2 in amyotrophic lateral sclerosis-related neurodegeneration.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Discovery of Tetrahydropyrazolopyrazine Derivatives as Potent and Selective MYT1 Inhibitors for the Treatment of Cancer

Breast and gynecological cancers are among the leading causes of death in women worldwide, illustrating the urgent need for innovative treatment options. We identified MYT1 as a promising new therapeutic target for breast and gynecological cancer using PandaOmics, an AI-driven target discovery platform. The synthetic lethal relationship of MYT1 in tumor cell lines with CCNE1 amplification enhanced this rationale. Through structure-based drug design, we developed a series of novel, potent, and highly selective inhibitors specifically targeting MYT1. Importantly, our lead compound, featuring a tetrahydropyrazolopyrazine ring, exhibits remarkable selectivity over WEE1, a related kinase associated with bone marrow suppression upon inhibition. Optimization of potency and physical properties resulted in the discovery of compound 21, a novel MYT1 inhibitor, exhibiting optimal pharmacokinetic properties and promising in vivo antitumor efficacy.

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Introduction

Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation

In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement

Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology

We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results

We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion

In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.

Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery

The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it possible to provide cost-effective, robust, secure, and scalable alternatives to the on-premise (on-prem) HPC via Cloud, Fog, and Edge computing. It has enabled recent state-of-the-art machine learning (ML) and artificial intelligence (AI)-based tools for drug discovery, such as BERT, BARD, AlphaFold2, and GPT. This chapter attempts to overview types of software architectures for developing scientific software or application with deployment agnostic (on-prem to cloud and hybrid) use cases. Furthermore, the chapter aims to outline how the innovation is disrupting the orthodox mindset of monolithic software running on on-prem HPC and provide the paradigm shift landscape to microservices driven application programming (API) and message parsing interface (MPI)-based scientific computing across the distributed, high-available infrastructure. This is coupled with agile DevOps, and good coding practices, low code and no-code application development frameworks for cost-efficient, secure, automated, and robust scientific application life cycle management.

A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.

Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony

High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact.

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

Lysosomal cystine export regulates mTORC1 signaling to guide kidney epithelial cell fate specialization

Differentiation is critical for cell fate decisions, but the signals involved remain unclear. The kidney proximal tubule (PT) cells reabsorb disulphide-rich proteins through endocytosis, generating cystine via lysosomal proteolysis. Here we report that defective cystine mobilization from lysosomes through cystinosin (CTNS), which is mutated in cystinosis, diverts PT cells towards growth and proliferation, disrupting their functions. Mechanistically, cystine storage stimulates Ragulator-Rag GTPase-dependent recruitment of mechanistic target of rapamycin complex 1 (mTORC1) and its constitutive activation. Re-introduction of CTNS restores nutrient-dependent regulation of mTORC1 in knockout cells, whereas cell-permeant analogues of L-cystine, accumulating within lysosomes, render wild-type cells resistant to nutrient withdrawal. Therapeutic mTORC1 inhibition corrects lysosome and differentiation downstream of cystine storage, and phenotypes in preclinical models of cystinosis. Thus, cystine serves as a lysosomal signal that tailors mTORC1 and metabolism to direct epithelial cell fate decisions. These results identify mechanisms and therapeutic targets for dysregulated homeostasis in cystinosis.

From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence

While genome-wide association studies (GWAS) have discovered thousands of disease-associated loci, molecular mechanisms for a considerable fraction of the loci remain to be explored. The logical next steps for post-GWAS are interpreting these genetic associations to understand disease etiology (GWAS functional studies) and translating this knowledge into clinical benefits for the patients (GWAS translational studies). Although various datasets and approaches using functional genomics have been developed to facilitate these studies, significant challenges remain due to data heterogeneity, multiplicity, and high dimensionality. To address these challenges, artificial intelligence (AI) technology has demonstrated considerable promise in decoding complex functional datasets and providing novel biological insights into GWAS findings. This perspective first describes the landmark progress driven by AI in interpreting and translating GWAS findings and then outlines specific challenges followed by actionable recommendations related to data availability, model optimization, and interpretation, as well as ethical concerns.

In Silico Exploration of Metabolically Active Peptides as Potential Therapeutic Agents against Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is regarded as a fatal neurodegenerative disease that is featured by progressive damage of the upper and lower motor neurons. To date, over 45 genes have been found to be connected with ALS pathology. The aim of this work was to computationally identify unique sets of protein hydrolysate peptides that could serve as therapeutic agents against ALS. Computational methods which include target prediction, protein-protein interaction, and peptide-protein molecular docking were used. The results showed that the network of critical ALS-associated genes consists of ATG16L2, SCFD1, VAC15, VEGFA, KEAP1, KIF5A, FIG4, TUBA4A, SIGMAR1, SETX, ANXA11, HNRNPL, NEK1, C9orf72, VCP, RPSA, ATP5B, and SOD1 together with predicted kinases such as AKT1, CDK4, DNAPK, MAPK14, and ERK2 in addition to transcription factors such as MYC, RELA, ZMIZ1, EGR1, TRIM28, and FOXA2. The identified molecular targets of the peptides that support multi-metabolic components in ALS pathogenesis include cyclooxygenase-2, angiotensin I-converting enzyme, dipeptidyl peptidase IV, X-linked inhibitor of apoptosis protein 3, and endothelin receptor ET-A. Overall, the results showed that AGL, APL, AVK, IIW, PVI, and VAY peptides are promising candidates for further study. Future work would be needed to validate the therapeutic properties of these hydrolysate peptides by in vitro and in vivo approaches.

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 μM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

Identification of potential pathways and biomarkers linked to progression in ALS

OBJECTIVE

To identify potential diagnostic and prognostic biomarkers for clinical management and clinical trials in amyotrophic lateral sclerosis.

METHODS

We analysed proteomics data of ALS patient-induced pluripotent stem cell-derived motor neurons available through the AnswerALS consortium. After stratifying patients using clinical ALSFRS-R and ALS-CBS scales, we identified differentially expressed proteins indicative of ALS disease severity and progression rate as candidate ALS-related and prognostic biomarkers. Pathway analysis for identified proteins was performed using STITCH. Protein sets were correlated with the effects of drugs using the Connectivity Map tool to identify compounds likely to affect similar pathways. RNAi screening was performed in a Drosophila TDP-43 ALS model to validate pathological relevance. A statistical classification machine learning model was constructed using ridge regression that uses proteomics data to differentiate ALS patients from controls.

RESULTS

We identified 76, 21, 71 and 1 candidate ALS-related biomarkers and 22, 41, 27 and 64 candidate prognostic biomarkers from patients stratified by ALSFRS-R baseline, ALSFRS-R progression slope, ALS-CBS baseline and ALS-CBS progression slope, respectively. Nineteen proteins enhanced or suppressed pathogenic eye phenotypes in the ALS fly model. Nutraceuticals, dopamine pathway modulators, statins, anti-inflammatories and antimicrobials were predicted starting points for drug repurposing using the connectivity map tool. Ten diagnostic biomarker proteins were predicted by machine learning to identify ALS patients with high accuracy and sensitivity.

INTERPRETATION

This study showcases the powerful approach of iPSC-motor neuron proteomics combined with machine learning and biological confirmation in the prediction of novel mechanisms and diagnostic and predictive biomarkers in ALS.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets.

METHODS

We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. The SHapley Additive exPlanations (SHAP values) were used for interpretation of the machine learning classifiers. The methodology was developed and tested using two distinct publicly available ALS RNA-seq datasets. We evaluated the performance of SES as a dimensionality reduction method against: (a) Least Absolute Shrinkage and Selection Operator (LASSO), and (b) Local Outlier Factor (LOF).

RESULTS

The proposed methodology achieved 85.18% accuracy for the classification of cerebellum or frontal cortex samples as C9orf72-related familial ALS, sporadic ALS or healthy samples. Importantly, the genes identified as the most determinative have also been reported as disease-associated in ALS literature. When tested in the evaluation dataset, the methodology achieved 88.89% accuracy for the classification of sporadic ALS motor neuron samples. When LASSO was used as feature selection method instead of SES, the accuracy of the machine learning classifiers ranged from 74.07 to 96.30%, depending on tissue assessed, while LOF underperformed significantly (77.78% accuracy for the classification of pooled cerebellum and frontal cortex samples).

CONCLUSIONS

Using SES, we addressed the challenge of high dimensionality in gene expression data analysis, and we trained accurate machine learning ALS classifiers, specific for the gene expression patterns of different disease subtypes and tissue samples, while identifying disease-associated genes.

Future directions in regulatory affairs

The field of regulatory affairs deals with the regulatory requirements for marketing authorization of therapeutic products. This field is facing a myriad of forces impacting all aspects of the development, regulation and value proposition of new therapeutic products. Changes in global megatrends, such as geopolitical shifts and the rise of the green economy, have emphasized the importance of manufacturing and supply chain security, and reducing the environmental impacts of product development. Rapid changes due to advances in science, digital disruption, a renewed focus on the centrality of the patient in all stages of therapeutic product development and greater collaboration between national regulatory authorities have been accelerated by the COVID-19 pandemic. This article will discuss the various trends that are impacting the development of new therapies for alleviating disease and how these trends therefore impact on the role of the regulatory affairs professional. We discuss some of the challenges and provide insights for the regulatory professional to remain at the forefront of these trends and prepare for their impacts on their work.

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.