Paradigm Shift In Drug Discovery Research Articles

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Biomedical Text Mining for Drug Discovery using Natural Language Processing and Deep Learning.

In the realm of drug discovery, the deluge of biomedical literature poses a formidable challenge for researchers to efficiently extract relevant information. Biomedical text mining, a multidisciplinary field at the intersection of computer science and bioinformatics, has emerged as a pivotal tool to unravel the vast troves of information concealed within scientific literature. This abstract explores the synergy between Natural Language Processing (NLP) and Deep Learning (DL) techniques in the context of biomedical text mining, aiming to expedite the drug discovery process. Natural Language Processing, with its capacity to comprehend and interpret human language, serves as the foundational layer for extracting meaningful insights from biomedical texts. Leveraging advanced NLP algorithms, researchers can identify key entities such as genes, proteins, diseases, and their interactions, providing a comprehensive understanding of the biological landscape. However, the complexity and ambiguity inherent in scientific texts demand a more sophisticated approach. Deep Learning, characterized by neural networks with multiple layers, has proven instrumental in handling intricate patterns and latent relationships within biomedical data. The confluence of NLP and DL not only aids in information extraction but also fosters the creation of predictive models for drug discovery. By training models on vast datasets, researchers can predict potential drug candidates, understand drug-disease associations, and unveil novel therapeutic targets. The collaborative approach presented in this abstract embodies a paradigm shift in drug discovery, where the amalgamation of linguistic comprehension and deep learning prowess enables researchers to navigate the labyrinth of biomedical literature with unprecedented efficiency. As the biomedical landscape continues to evolve, the proposed synergistic framework serves as a beacon for researchers and industry professionals, offering a potent toolkit to harness the wealth of information concealed within textual data, ultimately accelerating the drug discovery pipeline.

Revolutionizing Pharmaceutical Research: Harnessing Machine Learning for a Paradigm Shift in Drug Discovery

The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic transition in the field of pharmaceutical research and development. This study examines the several effects of machine learning (ML) on different phases of medication discovery, development, and patient care. The capability of ML to quickly process huge chemical libraries and forecast interactions with target proteins is studied, starting with compound screening and selection. The potential for fewer false positives and negatives, improved hit prediction accuracy, and ensemble technique use are underlined. The part that machine learning plays in enhancing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile is then explained. ML models anticipate compound actions inside the human body by analyzing molecular structures and characteristics, improving assessments of drug safety and efficacy. The article goes into further detail about predictive modeling, highlighting how machine learning may be used to find prospective therapeutic targets and confirm their applicability. The combination of multi-omics data, deep learning, and the possibility to identify similar molecular pathways across diseases highlight the game-changing potential of machine learning in this field. The article also covers the use of ML in clinical trials, highlighting its benefits for trial planning, patient recruitment, real-time monitoring, and individualized therapy predictions. By utilizing computational analysis and quantum physics, the power of machine learning-driven de novo drug creation is examined, revealing the potential to develop new therapeutic candidates. In this article, the ethical issues surrounding AI-driven drug discovery are discussed, with a focus on the necessity of transparent data utilization, human oversight, and responsible data consumption. The report ends by predicting ML's potential for pharmaceutical R&D in the future. Accelerated drug discovery pipelines, the rise of customized medicine powered by predictive models, optimized clinical trials, and a change in medication repurposing tactics are all envisaged in this. The report emphasizes the revolutionary potential of ML in altering pharmaceutical research and development while noting obstacles in data quality, model interpretability, ethics, and interdisciplinary collaboration. It is suggested that the ethical integration of AI technologies, interdisciplinary cooperation, and regulatory modifications are essential steps to unlock the full potential of ML and AI and, ultimately, provide patients throughout the world with safer, more efficient, and individualized treatments.

Advantages of X-ray Crystallography in Drug Discovery Research

The paradigm shift in drug discovery that began in the early 1990s was the development of Structure Based Drug Design（SBDD）as a means of rational drug design for disease targets. The greatest advantage of X-ray analysis is that once the crystal structure is obtained, structural information can be fed back very quickly under almost the same conditions for soaking and co-crystallization, contributing to lead optimization in the early stages of drug discovery. In addition, it is unique in that it covers target molecules from small molecular weights（20-30 kDa）to large molecular weights（100 kDa～）. On the other hand, it has many weak points such as dynamic structure close to the physiological state, identification of hydrogen atoms, and acquisition of charge information, which, when combined with multiple methods in a complementary manner, will contribute to a significant reduction in the lead optimization period in the future.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders.

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Polyethylenimine-grafted mesoporous silica nanocarriers markedly enhance the bactericidal effect of curcumin against Staphylococcus aureus biofilm.

The recalcitrant nature of biofilms makes biofilm‐associated infections difficult to treat in modern medicine. Biofilms have a high vulnerability to antibiotics and a limited repertoire of antibiotics could act on matured biofilms. This issue has resulted in a gradual paradigm shift in drug discovery and therapy, with anti‐biofilm compounds being sought alongside new drug carriers. A potential solution to biofilm‐associated infections is to employ antibiofilm treatments, which can attack biofilms from many fronts. Nanocarriers are promising in this regard because they can be entrapped within biofilm matrix, target biofilm matrix, and provide local drug delivery to inhibit biofilm formation. In this study, curcumin as an herbal extract was loaded onto hyperbranched polyethylenimine‐grafted mesoporous silica nanoparticles (F‐MSN‐PEI/Cur) and antibiofilm investigations were performed. The F‐MSN‐PEI/Cur design has the potential to repurpose curcumin as an antibiofilm agent by increasing its solubility and lowering the required doses for the destruction of matured biofilms as well as suppressing biofilm development. Using imaging and spectroscopic techniques, we assessed the interaction of F‐MSN‐PEI/Cur with Staphylococcus aureus bacterial cells and determined the impact of F‐MSN‐PEI/Cur on eradicating matured biofilms and suppressing biofilm development. The F‐MSN‐PEI/Cur design is highly cytocompatible, as observed by the cytotoxicity screening investigations on L929 mouse fibroblast cell line. Our findings show that F‐MSN‐PEI/Cur design reduces the bacterial cell viability, inhibits biofilm formation, and induces biofilm eradication, which is attributed to F‐MSN‐PEI/Cur design having the potential to repurpose the antibiofilm activity of curcumin‐herbal extract.

Discovery of CRBN as a target of thalidomide: a breakthrough for progress in the development of protein degraders.

Progress in strategies aimed at breaking down therapeutic target proteins has led to a paradigm shift in drug discovery. Thalidomide and its derivatives are the only protein degraders currently used in clinical practice. Our understanding of the molecular mechanism of action of thalidomide and its derivatives has advanced dramatically since the identification of cereblon (CRBN) as their direct target. The binding of thalidomide derivatives to CRBN, a substrate recognition receptor for Cullin 4 RING E3 ubiquitin ligase (CRL4), induces the recruitment of non-native substrates to CRL4CRBN and their subsequent degradation. This discovery was a breakthrough in the current rapid development of protein-degrading agents because clarification of the mechanism of action of thalidomide derivatives has demonstrated the clinical value of these compounds. This review provides an overview of the mechanism of action of thalidomide and its derivatives and describes perspectives for protein degraders.

Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery.

Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery. ATOM is developing and validating a precompetitive, preclinical, small molecule drug discovery platform that simultaneously optimizes pharmacokinetics, toxicity, protein-ligand interactions, systems-level models, molecular design, and novel compound generation. To achieve this, the ATOM Modeling Pipeline (AMPL) has been developed to enable advanced and emerging machine learning (ML) approaches to build models from diverse historical drug discovery data. This modular pipeline has been designed to couple with a generative algorithm that optimizes multiple parameters necessary for drug discovery. ATOM's approach is to consider the full pharmacology and therapeutic window of the drug concurrently, through computationally-driven design, thereby reducing the number of molecules that are selected for experimental validation. Here, we discuss the role of collaborative efforts such as consortia and public-private partnerships in accelerating cross disciplinary innovation and the development of open-source tools for drug discovery.

Drug Repurposing: A Paradigm Shift in Drug Discovery

The traditional methods of drug discovery and drug development are a tedious, complex, and costly process. Target identification, target validation; lead identification; and lead optimization are a lengthy and unreliable process that further complicates the discovery of new drugs. A study of more than 15 years reports that the success rate in the discovery of new drugs in the fields of ophthalmology, cardiovascular, infectious disease, and oncology to be 32.6%, 25.5%, 25.2% and 3.4%, respectively. A tedious and costly process coupled with a very low success rate makes the traditional drug discovery a less attractive option. Therefore, an alternative to traditional drug discovery is drug repurposing, a process in which already existing drugs are repurposed for conditions other than which were originally intended. Typical examples of repurposed drugs are thalidomide, sildenafil, memantine, mirtazapine, mifepristone, etc. In recent times, several databases have been developed to hasten drug repurposing based on the side effect profile, the similarity of chemical structure, and target site. This work reviews the pivotal role of drug repurposing in drug discovery and the databases currently available for drug repurposing.

Open Targets: Pre-competitive Collaborative Research in Life Sciences

Since the sequencing of the human genome a decade ago, we have witnessed a paradigm shift in drug discovery and development where firms have been forced to build a range of technological capabiliti...

Navigating tissue chips from development to dissemination: A pharmaceutical industry perspective.

Tissue chips are poised to deliver a paradigm shift in drug discovery. By emulating human physiology, these chips have the potential to increase the predictive power of preclinical modeling, which in turn will move the pharmaceutical industry closer to its aspiration of clinically relevant and ultimately animal-free drug discovery. Despite the tremendous science and innovation invested in these tissue chips, significant challenges remain to be addressed to enable their routine adoption into the industrial laboratory. This article describes the main steps that need to be taken and highlights key considerations in order to transform tissue chip technology from the hands of the innovators into those of the industrial scientists. Written by scientists from 13 pharmaceutical companies and partners at the National Institutes of Health, this article uniquely captures a consensus view on the progression strategy to facilitate and accelerate the adoption of this valuable technology. It concludes that success will be delivered by a partnership approach as well as a deep understanding of the context within which these chips will actually be used. Impact statement The rapid pace of scientific innovation in the tissue chip (TC) field requires a cohesive partnership between innovators and end users. Near term uptake of these human-relevant platforms will fill gaps in current capabilities for assessing important properties of disposition, efficacy and safety liabilities. Similarly, these platforms could support mechanistic studies which aim to resolve challenges later in development (e.g. assessing the human relevance of a liability identified in animal studies). Building confidence that novel capabilities of TCs can address real world challenges while they themselves are being developed will accelerate their application in the discovery and development of innovative medicines. This article outlines a strategic roadmap to unite innovators and end users thus making implementation smooth and rapid. With the collective contributions from multiple international pharmaceutical companies and partners at National Institutes of Health, this article should serve as an invaluable resource to the multi-disciplinary field of TC development.

ビッグデータがもたらす創薬のパラダイムシフト

ビッグデータがもたらす創薬のパラダイムシフト

A Platform of Anti-biofilm Assays Suited to the Exploration of Natural Compound Libraries.

Biofilms are regarded as one of the most challenging topics of modern biomedicine, and they are potentially responsible for over 80% of antibiotic-tolerant infections. Biofilms have displayed an exceptionally high tolerance for chemotherapy, which is thought to be multifactorial. For instance, the matrix provides a physical barrier that decreases the penetration of antibiotics into the biofilm. Also, cells within the biofilms are phenotypically diverse. Likely, biofilm resilience arises from a combination of these and other, yet unknown, mechanisms. All of the currently existing antibiotics have been developed against single-cells (planktonic) bacteria. Therefore, so far, a very limited repertoire of molecules exists that can selectively act on mature biofilms. This situation has driven a progressive paradigm shift in drug discovery, in which searching for anti-biofilms has been urged to occupy a more prominent place. An additional challenge is that there are a very limited number of standardized methods for biofilm research, especially those that can be used for large-throughput screening of chemical libraries. Here, an experimental anti-biofilm platform for chemical screening is presented. It uses three assays to measure biofilm viability (with resazurin staining), total biomass (with crystal violet staining), and biofilm matrix (using a wheat germ agglutinin, WGA-fluorescence-based staining of the poly-N-acetyl-glucosamine, PNAG, fraction). All the assays were developed using Staphylococcus aureus as the model bacteria. Examples of how the platform can be used for primary screening as well as for functional characterization of identified anti-biofilm hits are presented. This experimental sequence further allows for the classification of the hits based upon the measured end-points. It also provides information on their mode of action, especially on long-term versus short-term chemotherapeutic effects. Thus, it is very advantageous for the quick identification of high-quality hit compounds that can serve as starting points for various biomedical applications.

Herbal medicine as an alternative medicine for treating diabetes: the global burden.

Herbal medicine as an alternative medicine for treating diabetes: the global burden.

A data mining approach for identifying novel target specific small molecules

Background There has been a paradigm shift in drug discovery from being a single-target approach to a multi-target comparative analysis. This has shifted the emphasis from designing lead candidates with desirable pharmacokinetic properties against individual targets to synthesizing small molecules active at family and subfamily levels. Therefore, in the present study, protein targets and small molecule ligands available in PubChem BioAssay and DrugBank databases were comparatively analyzed to identify novel target specific small molecules.

New approaches for dissecting protease functions to improve probe development and drug discovery

Proteases are well-established targets for pharmaceutical development because of their known enzymatic mechanism and their regulatory roles in many pathologies. However, many potent clinical lead compounds have been unsuccessful either because of a lack of specificity or because of our limited understanding of the biological roles of the targeted protease. In order to successfully develop protease inhibitors as drugs, it is necessary to understand protease functions and to expand the platform of inhibitor development beyond active site-directed design and in vitro optimization. Several newly developed technologies will enhance assessment of drug selectivity in living cells and animal models, allowing researchers to focus on compounds with high specificity and minimal side effects in vivo. In this review, we highlight advances in the development of chemical probes, proteomic methods and screening tools that we feel will help facilitate this paradigm shift in drug discovery.

Review of Integrative Analysis Challenges in Systems Biology

In the pharmaceutical industry, systems biology is making a paradigm shift in drug discovery from target-focused to a systems approach. It studies the molecular level changes in a biological system as an integrated and interacting network of genes, proteins, and metabolites instead of focusing on individual components. Integrative analysis of molecular level data becomes very important in unraveling the complex relationship and interactions in the biological system. One way to conduct integrative analysis is to identify correlations among genes, proteins, and metabolites. There are various analytic challenges: data preprocessing from various omics platforms; the number of variables much larger than the number of subjects; wide differences in the number of variables per platform leading to imbalance in the platform contributions to the analysis; the issues of multicollinearity and multiple testing; and difficulty in proper model validation to avoid selection bias. This article is a comprehensive review of statistical methods that address analysis challenges in integrative data.

Drug Discovery by Formulation Design and Innovative Drug Delivery Systems (DDS)

This review describes studies on drug discovery using a rational formulation design and innovative, drug delivery systems (DDS) for biomaterials such as therapeutic peptides and nucleotides. The microcapsules of the LH-RH superagonist leuprorelin acetate prepared using the new in-water drying method and biodegradable polymers, such as PLGA and PLA, could achieve a long-term sustained release for 1-6 months thereby facilitating easily treatment of hormone-dependent diseases, prostate cancer, endometriosis, and precocious puberty. This DDS technology showed an improvement in patient QOL and highly promoted the clinical value of the agonist. Moreover, PLGA microcapsules of siRNAs against VEGF, cFLIP, Raf-1, and Int6 have also been developed to treat various cancers and arteriosclerosis obliterans. To develop therapeutic nucleotides, a particle design is created using functional peptides, such as cell penetrating peptides (CPP), nuclear localizing signals (NLS), tight junction reversible openers (AT1002), bombesin, and dynein light chain-associated sequences. siRNA use should lead to a paradigm shift in drug discovery against various diseases. Tat analog with NLS could enhance the potency of a vaginal DNA vaccine. The artificial Tat CPP of STR-CH(2)R(4)H(2)C synthesized in our laboratory could efficiently deliver siRNAs into many types of cells and enhance the therapeutic effects for treating sarcoma, atopic dermatitis, allergic rhinitis, and asthma by intratumor injection and inhalation of the nanoparticles. Tat and AT1002 analogs used to treat atopic dermatitis in mice increased cell membrane permeability to siRelA, a siRNA against a subclass of NF-κB, and exhibited striking therapeutic and preventive effects.

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

Knitting and untying the protein network: Modulation of protein ensembles as a therapeutic strategy

Proteins constitute the working machinery and structural support of all organisms. In performing a given function, they must adopt highly specific structures that can change with their level of activity, often through the direct or indirect action of other proteins. Indeed, proteins typically function within an ensemble, rather than individually. Hence, they must be sufficiently flexible to interact with each other and execute diverse tasks. The discovery that errors within these groups can ultimately cause disease has led to a paradigm shift in drug discovery, from an emphasis on single protein targets to a holistic approach whereby entire ensembles are targeted.