Drug Development Process Research Articles

A New Perspective on Drugs for Duchenne Muscular Dystrophy: Proposals for Better Respiratory Outcomes and Improved Regulatory Pathways.

New drugs for Duchenne muscular dystrophy (DMD) are emerging rapidly. However, we and others believe these drugs are achieving regulatory approval prematurely. It is the cardiorespiratory complications of DMD that cause the disease's major morbidities and that determine survival. Thus, to be truly effective, a new drug must improve cardiorespiratory function; instead, new drugs are approved for patient use via accelerated regulatory pathways that rely on surrogate outcome measures with unproven clinical benefits (such as tissue levels of non-biologic, truncated dystrophin) and on scales that reflect muscle strength (such as small improvements in timed activities). In DMD, cardiorespiratory complications occur in "older" individuals who are in the non-ambulatory stage of the disease. In contrast, accelerated approvals are based on data from young, ambulatory subjects, a group that essentially never experiences cardiorespiratory complications. When drug studies do obtain cardiorespiratory data, their methodologies are suboptimal. We critically review these methodologies in detail, including problems with the use of threshold levels of respiratory function as outcome measures; problems with the use of historical controls, whose results vary widely, and are influenced by uncontrolled variables related to their observational nature; and the limitations of using percent predicted forced vital capacity (FVC %pred), and its single rate of decline across a wide range of age and function, as a preferred respiratory outcome measure. We discuss the advantages of an alternative respiratory outcome, the absolute value of FVC with aging (the "Rideau plot"). Unlike FVC %pred, the Rideau plot considers distinct phenotypes rather than aggregating all individuals into a single respiratory trajectory. Key features of the Rideau plot can show the nature and timing of a drug's effect on respiratory function, making it a potentially better outcome measure for assessing the respiratory effects of a drug. With this article, we use our respiratory perspective to critically examine the DMD drug development process and to propose improvements in study methodologies and in the regulatory processes that approve new drugs.

Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification

Artificial intelligence (AI) shows great potential in medical diagnosis and drug analysis. Through deep learning technology, AI can automatically extract features from large amounts of complex medical data, significantly improving diagnostic accuracy and drug development efficiency. Especially in drug target discovery and antiviral peptide classification, AI technology can accelerate data processing and prediction, helping researchers identify potential therapeutic molecules more quickly and optimize the drug development process. This study proposes and validates a Deep learn-based model, deep-Avpiden, to improve the classification and discovery efficiency of antiviral peptides (AVPs). By using sequential convolutional networks (TCNs), the model outperforms traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in capturing long-term dependencies and parallel computing capabilities. In terms of dataset, we used AVPs and non-AVPS samples from multiple databases, totaling 5,414 cleaned and de-weighted peptide sequences for model training after data preprocessing and embedding. The Deep-AVPiden model has been shown to outperform existing advanced classifiers in experiments, and its effectiveness has been verified by accuracy, precision, recall rate, and area under the ROC curve (OC-ROC). In addition, to accommodate computing resource constraints, we propose an optimized version of Deep-AvPIDen (DS), which utilizes Deep separation convolution technology to significantly reduce computing resource consumption. Through the online application platform, researchers can efficiently classify antiviral proteins and discover new AVPs. Future research could further optimize the model's computational efficiency, handle larger data sets, and expand its potential for biomedical problems such as drug combination prediction and new drug discovery.

Enhanced targeting efficacy of baicalein analogues on the dimeric state of SARS-CoV-2 3CL protease compared to monomeric state

The COVID-19 pandemic caused by the novel coronavirus, SARS-CoV-2, has been a global threat affecting the entire world. It is a single-stranded RNA virus that belongs to the coronavirus family. In SARS-CoV2, the 3CL protease protein significantly contributes to viral replication and is responsible for viral polyprotein cleavage. These factors make 3CL protease a promising drug target to inhibit the growth of SARS-CoV-2. In this study, using in silico approaches, we have targeted the 3CL protease of SARS-CoV-2 to identify promising antiviral candidates for COVID-19 treatment. Here, 463 structural analogs of Baicalein compounds were collected initially, and by employing the quantitative structure-activity relationship (QSAR) technique on 76 antiviral compounds, screening was done against monomeric and dimeric versions of the target protein. Further, based on the molecular interaction studies and MD simulation, followed by validation of the obtained simulation trajectories using PCA and MM/PBSA calculation, it was observed that ligands showed better binding stability with dimeric proteins than monomeric proteins and can be used as suitable therapeutic candidates for SARS-CoV2 treatment. The MD simulation showed a favorable, robust outcome for the 46885476 when bound to the dimeric state. It matched the control in the number of hydrogen bonds and conformational stability. This molecule also directly impacted the catalytic dyads of the protein, suggesting potential inhibitory action. In addition, this study helps to accelerate the drug development process against SARS-CoV2 through the observed in-silico results, which need to be validated using clinical experiments in future studies.

AI-driven design and optimization of nanoparticle-based drug delivery systems

Nanoparticle-based drug delivery systems represent a transformative advancement in targeted therapeutics, providing meticulous drug delivery, enhanced bioavailability, and diminished side effects. However, designing nanoparticles (NPs) optimal for specific drugs and diseases remains a complex challenge. The advancements in artificial intelligence (AI) have provided innovative approaches to design and optimize these systems, improving their efficacy and adaptability. This review encompasses the integration of AI in the conceptualization and development of NP drug delivery systems, signifying its potential to revolutionize the field. The review discusses the different AI methods such as machine learning, neural networks, and optimization algorithms that simplify the fabrication of NPs with tailored characteristics such as size, surface chemistry, and drug release profiles. AI can also standardize these characteristics to enhance drug loading capacity, targeting specificity, and controlled release at the chosen site of action. AI-based predictive modeling enables the quick screening of numerous parameters, thus quickening the discovery of optimal NP configurations tailored to specific therapeutic needs. Furthermore, the review also discusses the case studies where AI has efficaciously forecasted NP behavior in biological environments, crucial for enhanced targeting and diminished off-target effects. The amalgamation of AI and nanotechnology not only streamlines the drug development process but also paves the way for personalized medicine. The review also entails the different challenges associated with implementing AI in this field, such as data quality, algorithm transparency, and regulatory specifications. By utilizing AI, researchers and healthcare providers can unlock new potentials in novel drug delivery systems, ultimately advancing the precision and effectiveness of treatments for various diseases. Finally, the review discusses the future directions of AI-based NP design, highlighting its benefits to transform drug delivery and augment patient outcomes.

A Review of Liquid Chromatography-Mass Spectrometry and its Applications in Chemical Analysis

Liquid Chromatography-Mass Spectrometry (LC-MS) has emerged as a powerful analytical technique combining the separation capabilities of liquid chromatography with the high sensitivity and selectivity of mass spectrometry. This sophisticated instrumentation enables precise identification and quantification of complex chemical mixtures across diverse fields including pharmaceuticals, environmental monitoring, food safety, and biological research. Modern LC-MS systems offer enhanced resolution, improved ionization methods, and sophisticated mass analyzers that facilitate accurate molecular weight determination and structural elucidation of compounds. Recent technological advancements have led to the development of ultra-high-performance liquid chromatography (UHPLC) systems coupled with high-resolution mass spectrometers, enabling faster analysis times and improved detection limits. The versatility of LC-MS is demonstrated through its applications in proteomics, metabolomics, drug development, and quality control processes. Integration of artificial intelligence and machine learning algorithms has further enhanced data processing capabilities, allowing for automated peak identification and complex mixture analysis. The continuous evolution of LC-MS technology has addressed previous limitations in ionization efficiency, matrix effects, and quantification accuracy. This analytical technique has revolutionized chemical analysis by providing comprehensive molecular information, making it an indispensable tool in modern analytical laboratories.

Role of Computer-Aided Design and Development in Modern Pharmaceuticals

The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.

Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

AbstractThe design and synthesis of drug molecules is a pivotal stage in drug development that traditionally requires significant investment in time and finances. However, the integration of artificial intelligence (AI) in drug design accelerates the identification of potential drug candidates, optimizes the drug development process, and contributes to more informed decision-making. The application of AI in molecular generation is changing the way researchers explore the chemical space and design novel compounds. It accelerates the process of drug discovery and materials science, enabling rapid exploration of the vast chemical landscapes for the identification of promising candidates for further experimental validation. The application of AI in predicting reaction products accelerates the synthesis planning process, contributes to the automation of synthetic chemistry tasks, and supports chemists in making informed decisions during drug discovery. This paper reviewed the recent advances in two interrelated areas: the application of AI in molecular generation and synthesis routes. It will provide insights into the innovative ways in which AI is transforming traditional approaches in drug development and predict its future progress in these key fields.

Navigating the oncology drug discovery and development process with programmes supported by the National Institutes of Health.

The translation of basic drug discoveries from laboratories to clinical use presents substantial challenges. Factors such as insufficient funding, misdirected project focus, and inability to understand a drug's limitations or strengths contribute to the difficulty of this process. To address these issues, the National Institutes of Health (NIH) has established various resources dedicated to streamlining drug development. The NIH offers access to regularly curated databases encompassing categories like drug discovery, target discovery, genomics, proteomics, and clinical datasets. The NIH also provides access to key resources through various programmes, such as the Developmental Therapeutics Program, focusing on preclinical drug discovery and the Cancer Therapy Evaluation Program, which oversees clinical trial efforts for investigational agents. These resources might include funding opportunities, access to a network of scientific experts, and services to address gaps in scientific work. This Review explores the diverse platforms and resources available at the NIH and outlines how researchers can leverage them to expedite the drug development process.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Character N-gram Model for Toxicity Prediction

Prediction of molecular toxicity plays an important role in drug discovery. It has a direct relationship with human health and medical destiny. Accurately assessing a molecule’s toxicity can aid in the weeding out of low-quality com pounds early in the drug discovery phase, avoiding depletion later in the drug development process. Computational models have been used to automatically predict the molecular toxicity. In this paper, a machine learning-based model has been proposed. TF/IDF representation scheme has been used for N-gram and integrated with SMILE. Multiple machine learning classifiers have been im plemented such as Decision Tree, KNN, MLP, Random Forests, SGD and SVM. RF outperforms all the other classifiers. A wide range of N-gram models have been implemented and trigram reported the best results.

Fighting Antimicrobial Resistance: Innovative Drugs in Antibacterial Research.

In the fight against bacterial infections, particularly those caused by multi-resistant pathogens, the need for new antibacterials is undoubted in scientific communities. and is by now also widely perceived by the general population. However, the antibacterial research landscape has changed considerably over the past years. The majority of big pharma companies has left the field and thus, the decline in R&D on antibacterials severely impacts the drug pipeline. In past years, antibacterial research increasingly relies on smaller companies or academic research institutions, which mostly have only limited financial resources, to carry a drug discovery and development process from the beginning and through the clinical phases. This review formulates the requirements for an antibacterial in regard of targeted pathogens and resistance mechanisms. Strategies are shown for the discovery of new antibacterial structures originating from natural sources, chemical synthesis and more recent AI approaches. This is complemented by principles for the computer-aided design of antibacterials and the refinement of a lead structure. The second part of the article comprises a compilation of promising antibacterial molecules classified according to bacterial target areas. Aspects of the origin, the antibacterial spectrum, resistance and the current development status of the presented drug molecules are highlighted.

Recent developments in antibiotic resistance: an increasing threat to public health

Abstract Antibiotic resistance (ABR) is a major global health threat that puts decades of medical progress at risk. Bacteria develop resistance through various means, including modifying their targets, deactivating drugs, and utilizing efflux pump systems. The main driving forces behind ABR are excessive antibiotic use in healthcare and agriculture, environmental contamination, and gaps in the drug development process. The use of advanced detection technologies, such as next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics, and metagenomics, has greatly improved the identification of resistant pathogens. The consequences of ABR on public health are significant, increased mortality rates, the endangerment of modern medical procedures, and resulting in higher healthcare expenses. It has been expected that ABR could potentially drive up to 24 million individuals into extreme poverty by 2030. Mitigation strategies focus on antibiotic stewardship, regulatory measures, research incentives, and raising public awareness. Furthermore, future research directions involve exploring the potential of CRISPR-Cas9 (CRISPR-associated protein 9), nanotechnology, and big data analytics as new antibiotic solutions. This review explores antibiotic resistance, including mechanisms, recent trends, drivers, and technological advancements in detection. It also evaluates the implications for public health and presents strategies for mitigating resistance. The review emphasizes the significance of future directions and research needs, stressing the necessity for sustained and collaborative efforts to tackle this issue.

An evaluation of the precision of computational methods used in drug development initiatives

Computational approaches are commonly employed to expedite and provide decision-making for the drug development process. Drug development programs that involve targets without known crystal structures can be quite challenging. In many cases, a viable approach is to generate reliable homology models using the amino acid sequence of the target. This is followed by a series of validation steps, druggable pocket detection, and then moving forward with lead identification and validation. This study commenced by conducting an initial benchmark exercise using a series of computationally designed sequences for steroid-binding proteins. By conducting an unbiased comparison with the released X-ray crystal structures, the homology models that were generated demonstrated reliable outcomes. The aligned homology models showed a root mean square deviation (RMSD) of less than 0.6 Å when compared to the corresponding X-ray structures. Three different methods were used to detect the druggable cavities for comparison, and the identified pockets closely resembled those of the crystal structures. The achievement of near-native pose prediction was made possible by utilizing the comprehensive binding energy function that characterizes the interaction between each pose and the neighboring residues. In order to address the issue of limited correlation between entropy and internal energy in docking, an alternative was devised by incorporating entropy as a post-docking optimization step to enhance the accuracy of ligand binding affinity predictions and improve the overall quality of the results.

The Integration of Artificial Intelligence in Drug Discovery and Development : Novel Approach

The drug discovery and development process is complex, time-consuming, and costly. Artificial Intelligence (AI) has emerged as a transformative technology to improve efficiency, accuracy, and innovation in pharmaceutical research. This study explores the applications, benefits, and challenges of integrating AI in drug discovery and development. the role of AI in drug discovery, its transformative impact on pharmaceutical research, and the potential benefits and challenges. Briefly mention the major AI techniques used in different phases of drug discovery and development. The integration of Artificial Intelligence (AI) into drug discovery and development is transforming the pharmaceutical industry by speeding up processes, reducing costs, and enhancing precision. This paper discusses the involvement of AI in drug discovery and development. AI has brought a revolution to drug invention and development, significantly reducing costs and accelerating the process. By integrating AI into these stages, drug development has become more efficient, allowing for faster and more cost-effective innovations in the pharmaceutical field.

Drug Repurposing for Cancer Treatment: A Comprehensive Review.

Cancer ranks among the primary contributors to global mortality. In 2022, the global incidence of new cancer cases reached about 20 million, while the number of cancer-related fatalities reached 9.7 million. In Saudi Arabia, there were 13,399 deaths caused by cancer and 28,113 newly diagnosed cases of cancer. Drug repurposing is a drug discovery strategy that has gained special attention and implementation to enhance the process of drug development due to its time- and money-saving effect. It involves repositioning existing medications to new clinical applications. Cancer treatment is a therapeutic area where drug repurposing has shown the most prominent impact. This review presents a compilation of medications that have been repurposed for the treatment of various types of cancers. It describes the initial therapeutic and pharmacological classes of the repurposed drugs and their new applications and mechanisms of action in cancer treatment. The review reports on drugs from various pharmacological classes that have been successfully repurposed for cancer treatment, including approved ones and those in clinical trials and preclinical development. It stratifies drugs based on their anticancer repurpose as multi-type, type-specific, and mechanism-directed, and according to their pharmacological classes. The review also reflects on the future potential that drug repurposing has in the clinical development of novel anticancer therapies.

Applications of Lead Compounds in Daily Life

Abstract. Lead compounds are crucial in the early stages of drug discovery and development and play a pivotal role in pharmaceutical advancements. In addition to drug development, lead compounds have a myriad of other applications in daily life. This article reviews the extensive applications of lead compounds across various fields and explores their potential uses. First, this paper introduced the concept, characteristics, and research significance of lead compounds. Next, we discuss in detail the application of lead compounds in fields such as medicine, agriculture, food engineering, and material science, exploring their importance and potential trends in these areas. In the medical field, lead compounds provide a foundation for new drug development. Through the drug development process, compounds with potential therapeutic effects have been discovered and optimized, offering new avenues for disease treatment. In agriculture, lead compounds play a significant role in the development of pesticides and plant protection, helping to enhance crop yield, resilience, and quality and promoting sustainable agricultural practices. In the food industry, lead compounds are widely used in the development of food additives, functional foods, and food preservation technologies, enhancing the nutritional value and safety of food products. In the realm of material science, the application of lead compounds has spurred the research and development of new materials, improved material performance and functionality, and broadened their application fields. However, with continuous advancements in science and technology and evolving societal needs, research on lead compounds still faces many challenges and opportunities. Thus, strengthening the research and broader application of lead compounds will become a critical direction for future scientific endeavors.

Metabolomics in pharmaceutical engineering: Bridging advanced analytics and precision medicine

Abstract. The integration of metabolomics into pharmaceutical engineering heralds a new frontier in drug development and precision medicine, offering insights into the intricate dynamics of metabolic responses to pharmaceuticals. This comprehensive review discusses the advancements in analytical technologies, such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, and their hyphenated forms, that have propelled the field of metabolomics. It delves into the utilization of multivariate data analysis, metabolic pathway analysis, and mathematical modeling to interpret complex metabolomic data, facilitating a deeper understanding of drug metabolism, efficacy, and toxicity. Furthermore, the article explores the pivotal role of pharmacometabolomics in personalizing therapy and the regulatory landscape shaping the integration of metabolomics in clinical settings. By highlighting case studies and current methodologies, this review underscores the significant potential of metabolomics in enhancing drug development processes, identifying biomarkers for therapeutic targeting, and advancing the goals of precision medicine.

A Target to Combat Antibiotic Resistance: Biochemical and Biophysical Characterization of 3-Dehydroquinate Dehydratase from Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is associated with the acquisition of nosocomial infections, community-acquired infections, and infections related to livestock animals. In the pursuit of molecular targets in the development process of antibacterial drugs, enzymes within the shikimate pathway, such as 3-dehydroquinate dehydratase (DHQD), are regarded as promising targets. Therefore, through biochemical and biophysical techniques, in the present work, the characterization of DHQD from MRSA (SaDHQD) was performed. The kinetic results showed that the enzyme had a Vmax of 107 μmol/min/mg, a Km of 54 μM, a kcat of 48 s−1, and a catalytic efficiency of 0.9 μM−1 s−1. Within the biochemical parameters, the enzyme presented an optimal temperature of 55 °C and was thermostable at temperatures from 10 to 20 °C, being completely inactivated at 60 °C in 10 min. Furthermore, SaDHQD showed an optimal pH of 8.0 and was inactivated at pH 4.0 and 12.0. Moreover, the activity of the enzyme was affected by the presence of ions, surfactants, and chelating agents. The thermodynamic data showed that the rate of inactivation of the enzyme was a temperature-dependent process. Furthermore, the enthalpy change, entropy change, and Gibbs free energy change of inactivation were positive and practically constant, which suggested that the inactivation of SaDHQD by temperature was driven principally by enthalpic contributions. These results provide, for the first time, valuable information that contributes to the knowledge of this enzyme and will be useful in the search of SaDHQD inhibitors that can serve as leads to design a new drug against MRSA to combat antibiotic resistance.

Large-scale characterization of drug mechanism of action using proteome-wide thermal shift assays.

In response to an ever-increasing demand of new small molecules therapeutics, numerous chemical and genetic tools have been developed to interrogate compound mechanism of action. Owing to its ability to approximate compound-dependent changes in thermal stability, the proteome-wide thermal shift assay has emerged as a powerful tool in this arsenal. The most recent iterations have drastically improved the overall efficiency of these assays, providing an opportunity to screen compounds at a previously unprecedented rate. Taking advantage of this advance, we quantified more than one million thermal stability measurements in response to multiple classes of therapeutic and tool compounds (96 compounds in living cells and 70 compounds in lysates). When interrogating the dataset as a whole, approximately 80% of compounds (with quantifiable targets) caused a significant change in the thermal stability of an annotated target. There was also a wealth of evidence portending off-target engagement despite the extensive use of the compounds in the laboratory and/or clinic. Finally, the combined application of cell- and lysate-based assays, aided in the classification of primary (direct ligand binding) and secondary (indirect) changes in thermal stability. Overall, this study highlights the value of these assays in the drug development process by affording an unbiased and reliable assessment of compound mechanism of action.

Application of Nexus Learning framework for Personalized Medicine and Drug Development

Biological systems provide an enormous amount of knowledge on development and disease. The pharmaceutical industry is facing new possibilities and challenges with the rise of high-throughput methods to study disease and biology. The aim is to develop drugs based on acceptable therapeutic assumptions. Nexus Learning provides a variety of tools that help improve discovery and decision making using extensive, high-quality data and well-specified queries. Potential applications of NLF can be found across the whole drug development process. Clinical study target validation, prognostic biomarker discovery, and digital pathology data processing are a few examples. There has been a wide variety of applications in terms of technique and environment, with some methods producing reliable forecasts and insights. The primary problem with using NLF is that the findings provided by NL tend to be easy to understand or reproduce, which can limit their value. The collection of complete and methodical high-dimensional data remains an essential need across all domains. By addressing these challenges and increasing understanding about what is required to verify NLF methodologies, we can employ NLF to make data-driven decisions, which can speed up drug discovery and development and minimize failure rates.