Current Drug Discovery Technologies - Volume 22, Issue 4, 2025

Unregulated cell division is one of the main causes of cancer. These cancerous cells negatively impact nearby healthy cells. Cancer can occur anywhere in the body. Normal cell division occurs when cells grow, reproduce, and divide as the body needs. As a normal cascade of cell growth and division, when the cells get damaged, they undergo death, and normal cells develop. However, sometimes, this process is not followed, and abnormal or damaged cells start to grow and multiply several times more than normal. This particular process may form the basis of cancer. There is a research gap in terms of identifying personalized synthetic anticancer therapy, which may be based on individual patient characteristics with an aim to optimize treatment efficacy and minimize adverse effects. While searching for new bioactive compounds, it has been observed that organic molecules with benzoic acid (BA) moiety possess significant anticancer potential. Several works of literature reported the use of BA from natural or synthetic sources to synthesize bioactive chemicals. It has been observed that several natural products also contain BA moiety, and the presence of this moiety is considered responsible for several important biological activities. Therefore, in order to chemically synthesize a wide variety of potent biologically active compounds, benzoic acid as a basic moiety in the form of a scaffold can be employed. Other synthetic compounds with BA scaffolds include furosemide, tetracaine, and bumetanide. The current article aims to focus on past and present work done on BA derivatives and to emphasize the molecular pathways involved in cancer treatment. The future prospects for research in this area are encouraging as researchers are striving to advance synthetic BA derivatives. This could possibly contribute to more efficient treatments and better results for cancer patients.

Most of the newly discovered drug candidates are lipophilic and poorly water-soluble, making it a significant challenge for the pharmaceutical industry to formulate suitable drug delivery systems. This review gives insight into an overview of the liquisolid technique (LST) and summarizes the progress of its various applications in drug delivery. This novel technique involves converting liquid drugs or drugs in a liquid state (such as solutions, suspensions, or emulsions) into dry, non-adherent, free-flowing, and readily compressible powder mixtures by blending or spraying a liquid dispersion onto specific powder carriers and coating materials. In Liquisolid systems, the liquid medication is absorbed into the interior framework of carriers. Once the carrier's interior is saturated with liquid medication, a liquid layer forms on the surface of the carrier particles, which is instantly adsorbed by the fine coating material. As a result, a dry, free-flowing, and compressible powder mixture is formed. Compared to other solubility enhancement techniques, s.a. micronization, inclusion complexation, microencapsulation, nanosuspension, and self-nano emulsions, LST is relatively simple to prepare and may offer a cost-effective solution to enhance the solubility of poorly water-soluble drugs enhancing its bioavailability in drug formulation and delivery.

The document emphasizes the importance of addressing key issues in Quality Management Systems (QMS) to stay competitive and provide high-quality goods and services in a dynamic market. It highlights the need to adopt novel quality management techniques for long-term success. The fundamentals of Quality by Design (QbD) are discussed, tracing back to pioneers like Joseph M. Juran and the Six Sigma concept for effective implementation. QbD is described as a systematic approach focusing on risk management, Quality Target Product Profile (QTPP), and Critical Quality Attributes (CQAs) to create products meeting predetermined quality standards. QbD incorporates quality concepts into the development and production processes. Its goal is to guarantee the quality of the product by determining and managing important factors. Manufacturers can be flexible with it while still adhering to strict quality standards. It signifies a change in the industry's paradigm.

In medicine, bioavailability is the percentage of a drug that enters the bloodstream and can be used to treat a patient. It has proven challenging throughout time to develop techniques that allow oral administration of most drugs, regardless of their properties, to achieve therapeutic systemic availability. This will be an impressive feat, considering that over 90% of pharmaceuticals are known to have limitations on their oral bioavailability. Improving bioavailability is crucial for optimizing the efficacy and safety of drugs. This review covers a wide range of techniques, including physical, chemical, and formulation approaches, highlighting their mechanisms, advantages, and limitations. Inhibitions of efflux pumps, inhibition of presystemic metabolism, and innovative drug delivery systems that capitalize on the gastrointestinal regionality of medicines are some of the new techniques that have drawn increased interest. Nanotechnology in pharmaceuticals is also being used in this field. We have collected the literature data from 2009 to 2024 using Science Direct, PubMed/Medline, Scopus, and Google Scholar.

AI's rise has affected many aspects of civilization. Pharmaceutical businesses have been hit hard. This review paper highlights AI's benefits for disease detection, clinical trials, medicine development, and productivity in the pharmaceutical industry. Pharmaceutical companies have built specialized systems to help doctors diagnose and monitor medication remediation. Pharmaceutical businesses are utilizing AI for machine learning to imitate human analytical processes for more accurate and insightful data. AI has many benefits for the pharmaceutical business. Data analysis can address previously insoluble problems due to improved precision. AI boosts productivity, lowers expenses, and enhances tasks. AI insights enhance understanding of user behavior, market performance, and clinical trial success. AI helps identify patients during clinical trials and improves antiviral detection to ensure efficacy, safety, cost-effectiveness, and seamless pharmaceutical procedures. The pharmaceutical industry emphasizes AI in R&D, drug discovery, diagnostics, sickness prevention, epidemic forecasting, remote access, manufacturing, and marketing. Artificial intelligence has transformed medication development and discovery by analyzing vast datasets, discovering complicated patterns, and forecasting efficacy. Pharmaceutical companies like Novartis, AstraZeneca, and Verge Genomics are utilizing AI for drug feature prediction, neurological evaluation, therapy development, pulmonary and hypertension recognition, low-cost medication production, and disease diagnosis.

Artificial intelligence (AI) is one of the fastest-growing fields in various industries, including engineering, architecture, medical and clinical research, aerospace, and others. AI, which is a combination of machine learning (ML), deep learning (DL), and human intelligence (HI), is revolutionizing drug discovery and development by making it more cost-effective and efficient. It is also being used in fields such as medicinal chemistry, molecular and cell biology, pharmacology, pharmacokinetics, formulation development, and toxicology. AI plays a crucial role in clinical testing by enhancing patient stratification, patient sample evaluation, and trial design, assisting in the identification of biomarkers, determining efficacy criteria, dose selection, trial length, and target patient population selection. The primary objective of this study is to emphasize the importance of AI in clinical trials and drug development, while also exploring the existing challenges and potential advancements in AI within the healthcare industry. A comprehensive literature review was conducted, covering the period from 1998 to 2023. The Science Direct, PubMed, and Google Scholar databases were searched for relevant information. A variety of publications, including Research Gate, Nature, MDPI, and Springer Link, provided pertinent data. This study aimed to gain a deeper understanding of the use of AI in clinical research and drug development, as well as its potential and limitations. We also discuss the benefits and main data limitations of the traditional trial and drug development approach. AI approaches are currently being used to overcome research obstacles and eliminate conceptual or methodological limitations. After discussing possible obstacles and coping mechanisms, we provide several recommendations to help individuals understand the challenges and difficulties associated with clinical research and drug development. It is essential for pharmaceutical companies to have a cutting-edge AI strategy if AI is to become a routine tool for clinical research and drug development.

Psoriasis is a prevalent inflammatory disease affecting almost 0.5-5% of the world population. Multiple treatment approaches have been developed to manage psoriasis so far. Although concerns exist in the long-term usage of conventional and biological agents in terms of safety, effectiveness, expensiveness, and tolerability, complementary and alternative medicine (CAM) is a promising point of view for future psoriasis management. In this study, databases including Scopus, PubMed, Google Scholar, and Web of Science were searched for relevant literature on herbal medications clinically evaluated for psoriasis, especially those originating from traditional medicine. About 40 relevant papers were selected by March 2023. Most of the studies were clinical trials on poly-herbal formulations from traditional Chinese medicine (TCM). There are controversial results regarding the efficacy of these herbal formulations in psoriasis mainly due to the variation in the study design. Moreover, the probable protective mechanisms and responsible herbal metabolites of these formulations are summarized. There is a global need for more in-vitro and in-vivo studies based on the standard protocols in terms of the evaluation of the safety and efficacy of topical/ systemic herbal preparations for psoriasis.

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the increasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polypharmacology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based virtual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided assistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effective drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the descriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

Diabetes is a chronic metabolic disease of high levels of glucose in the blood and affecting 536.6 million people in the world between the age group of 20-79 with management spent of 11% of the total worldwide. Wound healing in diabetics is impaired due to many factors like high blood sugar, poor blood circulation, damaged blood vessels, diabetic neuropathy, decreased immune responses etc. The presently used synthetic drugs have high costs, a toxic nature, and are full of adverse effects drawing attention to the need to identify new and successful treatment approaches for diabetic wounds. In silico drug screening methods of drug development made it easy to screen thousands of active constituents against a target specifically responsible for diabetes and wound healing. Thus the current review compiled the naturally available active compounds screened by in silico docking from natural resources and has the potential to treat diabetic wound healing with their specificity and target-based mechanism. This information will be helpful for further screening of non-reported natural compounds having antidiabetic as well as wound healing potential.