Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Targeted Protein Degradation (TPD) for Immunotherapy: Understanding Proteolysis Targeting Chimera-Driven Ubiquitin-Proteasome Interactions

Targeted protein degradation or TPD, is rapidly emerging as a treatment that utilizes small molecules to degrade proteins that cause diseases. TPD allows for the selective removal of disease-causing proteins, including proteasome-mediated degradation, lysosome-mediated degradation, and autophagy-mediated degradation. This approach has shown great promise in preclinical studies and is now being translated to treat numerous diseases, including neurodegenerative diseases, infectious diseases, and cancer. This review discusses the latest advances in TPD and its potential as a new chemical modality for immunotherapy, with a special focus on the innovative applications and cutting-edge research of PROTACs (Proteolysis TArgeting Chimeras) and their efficient translation from scientific discovery to technological achievements. Our review also addresses the significant obstacles and potential prospects in this domain, while also offering insights into the future of TPD for immunotherapeutic applications.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Crbn-based molecular Glues: Breakthroughs and perspectives

CRBN is a substrate receptor for the Cullin Ring E3 ubiquitin ligase 4 (CRL4) complex. It has been observed that CRBN can be exploited by small molecules to facilitate the recruitment and ubiquitination of non-natural CRL4 substrates, resulting in the degradation of neosubstrate through the ubiquitin-proteasome system. This phenomenon, known as molecular glue-induced protein degradation, has emerged as an innovative therapeutic approach in contrast to traditional small-molecule drugs. One key advantage of molecular glues, in comparison to conventional small-molecule drugs adhering to Lipinski's Rule of Five, is their ability to operate without the necessity for specific binding pockets on target proteins. This unique characteristic empowers molecular glues to interact with conventionally intractable protein targets, such as transcription factors and scaffold proteins. The ability to induce the degradation of these previously elusive targets by hijacking the ubiquitin-proteasome system presents a promising avenue for the treatment of recalcitrant diseases. Nevertheless, the rational design of molecular glues remains a formidable challenge due to the limited understanding of their mechanisms and actions. This review offers an overview of recent advances and breakthroughs in the field of CRBN-based molecular glues, while also exploring the prospects for a systematic approach to designing these compounds.

AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.

Harmony of Protein Tags and Chimeric Molecules Empowers Targeted Protein Ubiquitination and Beyond

Post-translational modifications (PTMs) are crucial mechanisms that underlie the intricacies of biological systems and disease mechanisms. This review focuses on the latest advancements in the design of heterobifunctional small molecules that hijack PTM machineries for target-specific modifications in living systems. A key innovation in this field is the development of proteolysis-targeting chimeras (PROTACs), which promote the ubiquitination of target proteins for proteasomal degradation. The past decade has seen several adaptations of the PROTAC concept to facilitate targeted (de)phosphorylation and acetylation. Protein fusion tags have been particularly vital in these proof-of-concept studies, aiding in the investigation of the functional roles of post-translationally modified proteins linked to diseases. This overview delves into protein-tagging strategies that enable the targeted modulation of ubiquitination, phosphorylation, and acetylation, emphasizing the synergies and challenges of integrating heterobifunctional molecules with protein tags in PTM research. Despite significant progress, many PTMs remain to be explored, and protein tag-assisted PTM-inducing chimeras will continue to play an important role in understanding the fundamental roles of protein PTMs and in exploring the therapeutic potential of manipulating protein modifications, particularly for targets not yet addressed by existing drugs.

Current strategic trends in drug discovery: the present as prologue

INTRODUCTION

Escalating costs and inherent uncertainties associated with drug discovery invite initiatives to improve its efficiency and de-risk campaigns for inventing better therapeutics. One such initiative involves recognizing and exploiting current approaches in therapeutics invention with molecular mechanisms of action that hold promise for designing and targeting new chemical entities as drugs.

AREAS COVERED

This perspective considers the current contextual framework around three drug-discovery approaches and evaluates their potential to help identify new targets/modalities in small-molecule molecular pharmacology: diversifying ligand-directed phenotypes for G protein-coupled receptor (GPCR) pharmacotherapeutic signaling; developing therapeutic-protein degraders and stabilizers for proximity-inducing pharmacology; and mining organelle biology for druggable therapeutic targets.

EXPERT OPINION

The contemporary drug-discovery approaches examined appear generalizable and versatile to have applications in therapeutics invention beyond those case studies discussed herein. Accordingly, they may be considered strategic trends worthy of note in advancing the field toward novel ways of addressing pharmacotherapeutically unmet medical needs.

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.

Function, mechanism and drug discovery of ubiquitin and ubiquitin-like modification with multiomics profiling for cancer therapy

Ubiquitin (Ub) and ubiquitin-like (Ubl) pathways are critical post-translational modifications that determine whether functional proteins are degraded or activated/inactivated. To date, >600 associated enzymes have been reported that comprise a hierarchical task network (e.g., E1-E2-E3 cascade enzymatic reaction and deubiquitination) to modulate substrates, including enormous oncoproteins and tumor-suppressive proteins. Several strategies, such as classical biochemical approaches, multiomics, and clinical sample analysis, were combined to elucidate the functional relations between these enzymes and tumors. In this regard, the fundamental advances and follow-on drug discoveries have been crucial in providing vital information concerning contemporary translational efforts to tailor individualized treatment by targeting Ub and Ubl pathways. Correspondingly, emphasizing the current progress of Ub-related pathways as therapeutic targets in cancer is deemed essential. In the present review, we summarize and discuss the functions, clinical significance, and regulatory mechanisms of Ub and Ubl pathways in tumorigenesis as well as the current progress of small-molecular drug discovery. In particular, multiomics analyses were integrated to delineate the complexity of Ub and Ubl modifications for cancer therapy. The present review will provide a focused and up-to-date overview for the researchers to pursue further studies regarding the Ub and Ubl pathways targeted anticancer strategies.