Pharmaceutical Research Research Articles

Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.

The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and disease complexities to predict clinical endpoints and biomarkers is central to MID3. QSP modeling has proven successful in metabolic and cardiovascular diseases and has expanded into oncology, immunotherapy, and infectious diseases. Despite its benefits, QSP model validation through clinical trial simulations using virtual patients (VPs) is challenging because of parameter variability and high computational costs. To address these challenges, this study proposes a hybrid method that combines Bayesian optimization with machine learning for efficient parameter screening. Our approach achieved an acceptance rate of 27.5% in QSP simulations, which is in sharp contrast with the 2.5% rate of conventional random search methods, indicating more than 10-fold improvement in efficiency. By facilitating faster and more diverse VPs generation, this method promises to advance clinical trial simulations and accelerate drug development in pharmaceutical research.

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope.

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

Data mining applications in risk research: A systematic literature review

Despite the rising literature on data mining (DM) approaches, there is a lack of a complete literature review and categorization system within risk research. This paper presents the first recognized academic literature review on the application of data mining tools in risk research provides an up-to-date SCOPUS literature database. Based on bibliometric analysis, 5422 papers related torisk were identified from a total of 77,410 studies on data mining and thoroughly analyzed. Each of the selected 5422 papers was classified into four risk categories: global risk, public health risk, molecular and biomedical risk, and pharmaceutical risk. Each primary risk category was further subdivided to highlight the specific research focuses within each domain. Global risks encompass business, environmental, and social risks. Scholars have predominantly focused on the banking, market, and construction sectors within business risk, while environmental risk includes catastrophe-related risks. Social risks encompass areas such as education, traffic safety, and transportation concerns. Clinical data is usually employed in public health risk research, while various radiomic databases are utilized in genetic and molecular biology research. In pharmaceutical research, DM is primarily used to detect adverse drug effects. According to the findings of this review, the fields of computer science and medicine received the most significant research attention. The review also discusses limitations and provides a roadmap to guide future research, aiming to enhance knowledge development related to the application of data mining techniques in risk-related studies.

Optimizing ex vivo penetration tests via quantitative confocal Raman spectroscopy: Impact of incubation time, skin hydration, surfactant treatment and UVA irradiation on caffeine distribution

Ex vivo penetration tests are important tools in cosmetic and pharmaceutical research. However, variability of experimental setups is challenging when reviewing literature. Different skin models, pre-treatments and experimental parameters render comparison difficult. Thus, our aim was to conduct ex vivo penetration tests using caffeine in different setups with varying incubation conditions (ambient vs. Franz cells, infinite vs. finite dose). Additionally, the impact of skin pre-treatment with different aggressors (surfactants, UVA irradiation) should be considered. Possible synergistic barrier damage of surfactants and UVA irradiation should be explored. Analysis was conducted using quantitative confocal Raman spectroscopy. Results showed that incubation time and extensive hydration (20 h in Franz cells) had the greatest impact on penetration behavior. Additional irradiation after pre-treatment with oil-in-water nanoemulsions showed no strong impact on caffeine penetration in general, irrespective of surfactant type. However, in case of sodium lauryl ether sulfate, a trend towards enhanced values was observed due to irradiation (1.3-fold). This suggests cumulative skin barrier damage of irritant surfactants and UVA irradiation, potentially due to stratum corneum alterations. Further studies using different irradiation regimens are planned to confirm this hypothesis.

An Overview of Metallic Nanoparticles: Classification, Synthesis, Applications, and their Patents.

Nanotechnology has gained enormous attention in pharmaceutical research. Nanotechnology is used in the development of nanoparticles with sizes ranging from 1-100 nm, with several extraordinary features. Metallic nanoparticles (MNPs) are used in various areas, such as molecular biology, biosensors, bio imaging, biomedical devices, diagnosis, pharmaceuticals, etc., for their specific applications. For this study, we have performed a systematic search and screening of the literature and identified the articles and patents focusing on various physical, chemical, and biological methods for the synthesis of metal nanoparticles and their pharmaceutical applications. A total of 174 references have been included in this present review, of which 23 references for recent patents were included. Then, 29 papers were shortlisted to describe the advantages, disadvantages, and physical and chemical methods for their synthesis, and 28 articles were selected to provide the data for biological methods for the formulation of metal NPs from bacteria, algae, fungi, and plants with their extensive synthetic procedures. Moreover, 27 articles outlined various clinical applications of metal NPs due to their antimicrobial and anticancer activities and their use in drug delivery. Several reviews are available on the synthesis of metal nanoparticles and their pharmaceutical applications. However, this review provides updated research data along with the various methods employed for their development. It also summarizes their various advantages and clinical applications (anticancer, antimicrobial drug delivery, and many others) for various phytoconstituents. The overview of earlier patents by several scientists in the arena of metallic nanoparticle preparation and formulation is also presented. This review will be helpful in increasing the current knowledge and will also inspire to innovation of nanoparticles for the precise and targeted delivery of phytoconstituents for the treatment of several diseases.

Mass Spectrometry Imaging for Spatial Toxicology Research.

The spatial information of xenobiotics distribution, metabolism, and toxicity mechanisms insitu has drawn increasing attention in both pharmaceutical and environmental toxicology research to aid drug development and environmental risk assessments. Mass spectrometry imaging (MSI) provides a label-free, multiplexed, and high-throughput tool to characterize xenobiotics, their metabolites, and endogenous molecules insitu with spatial resolution, providing knowledge on spatially resolved absorption, distribution, metabolism, excretion, and toxicity on the molecular level. In this perspective, we briefly summarize applications of MSI in toxicology on xenobiotic distribution and metabolism, quantification, toxicity mechanisms, and biomarker discovery. We identified several challenges regarding how we can fully harness the power of MSI in both fundamental toxicology research and regulatory practices. First, how can we increase the coverage, sensitivity, and specificity in detecting xenobiotics and their metabolites in complex biological matrices? Second, how can we link the spatial molecular information of xenobiotics to toxicity consequences to understand toxicity mechanisms, predict exposure outcomes, and aid biomarker discovery? Finally, how can we standardize the MSI experiment and data analysis workflow to provide robust conclusions for regulation and drug development? With these questions in mind, we provide our perspectives on the future directions of MSI as a promising tool in spatial toxicology research.

Revolutionizing Brain Drug Delivery: Buccal Transferosomes on the Verge of a Breakthrough.

The buccal cavity, also known as the oral cavity, is a complex anatomical structure that plays a crucial role in various physiological processes. It serves as a gateway to the digestive system and facilitates the initial stages of food digestion and absorption. However, its significance extends beyond mere digestion as it presents a promising route for drug delivery, particularly to the brain. Transferosomes are lipid-based vesicles that have gained significant attention in the field of drug delivery due to their unique structure and properties. These vesicles are composed of phospholipids that form bilayer structures capable of encapsulating both hydrophilic and lipophilic drugs. Strategies for the development of buccal transferosomes for brain delivery have emerged as promising avenues for pharmaceutical research. This review aims to explore the various approaches and challenges associated with harnessing the potential of buccal transferosomes as a means of enhancing drug delivery to the brain. By understanding the structure and function of both buccal tissue and transferosomes, researchers can develop effective formulation methods and characterization techniques to optimize drug delivery. Furthermore, strategic approaches and success stories in buccal transferosome development are highlighted, showcasing inspiring examples that demonstrate their potential to revolutionize brain delivery.

Autoregressive HMM resolves biomolecular transitions from passive optical tweezer force measurements

Optical tweezer (OT) single molecule force spectroscopy is a powerful method to map out the energy landscape of biological complexes and has found increasing applications in academic and pharmaceutical research. The dominant method to extract molecular conformation transitions from the thermal diffusion-broadened trajectories of the microscopic OT probes attached to the single molecule of interest is through hidden Markov models (HMM). In standard applications, the HMMs assume a white noise spectrum of the probes superimposed onto the molecular signal.Here, we demonstrate, through theoretical derivation, computer modeling and experimental measurements that this standard white noise HMM (wnHMM) misses key features of real OT data. The deviation is most pronounced at higher frequencies because the white noise model does not account for the over-damped nature of particle diffusion in an OT harmonic potential in aqueous environments. To address this, we derive how to incorporate autoregression (ar) between consecutive data points into an HMM, and demonstrate through modeling and experiment that such an arHMM captures real OT data behavior across all frequency ranges. Through analysis of real OT data we recorded on a single DNA hairpin undergoing folding and unfolding transitions, we show that the wnHMM extracts lifetimes that are at least a factor of 2 faster and less consistent than the arHMM results which match expectations and prior measurements. Overall, our work suggests that arHMM should be the default model choice for analysis OT single molecule transitions and that its use will improve the fidelity and accuracy of single molecule force spectroscopy measurements.

Application of continuous wavelet transforms for simultaneous estimation of domperidone and lansoprazole in capsule formulations

UV–Vis spectroscopy remains essential in pharmaceutical research and quality control due to its accessibility, simplicity, and effectiveness. However, overlapping spectra resulting from multicomponent drug formulations present challenges. Here, we describe a signal processing strategy using continuous wavelet transform (CWT) to resolve overlapping spectra of domperidone (DMP) and lansoprazole (LSP) for simultaneous quantification. Three different wavelet functions (Symlets 3, Coiflets 1, Daubechies 10), were found to be suitable for this purpose. Linear calibration curves were constructed using the CWT zero-crossing technique and the methods were validated by analyzing a set of synthetic mixtures, intra-and inter-day samples, and standard addition samples. The assay results of commercial capsule samples were compared with those obtained by derivative spectroscopy, and no significant statistical difference was observed. The proposed CWT methods demonstrated good compliance with the label claims and proved to be reliable, versatile, fast, and cost-efficient analytical methods without the need for preliminary separation procedures.

A Review on Microengineering of Epithelial Barriers for Biomedical and Pharmaceutical Research.

Epithelial tissue forms a barrier around the human body and visceral organs, providing protection, permeation, sensation, and secretion. It is vital for our sustenance as it protects the tissue from harm and injury by restricting the entry of foreign bodies inside. Furthermore, it is a strong barrier to drugs, nutrients, and other essential deliverables. This layer also houses a large consortium of microbes, which thrive in tandem with human tissue, providing several health benefits. Moreover, the complex interplay of the microbiome with the barrier tissue is poorly understood. Therefore, replicating these barrier tissues on microdevices to generate physiological and pathophysiological models has been a huge interest for researchers over the last few decades. The artificially engineered reconstruction of these epithelial cellular barriers on microdevices could help underpin the host-microbe interaction, generating a physiological understanding of the tissue, tissue remodeling, receptor-based selective diffusion, drug testing, and others. In addition, these devices could reduce the burden of animal sacrifices for similar research and minimize the failure rate in drug discovery due to the use of primary human cells and others. This review discusses the nature of the epithelial barrier at different tissue sites, the recent developments in creating engineered barrier models, and their applications in pathophysiology, host-microbe interactions, drug discovery, and cytotoxicity. The review aims to provide know-how and knowledge behind engineered epithelial barrier tissue to bioengineers, biotechnologists, and scientists in allied fields.

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.

Enzymatic Cleavage of Double-Stranded DNA-Encoded Libraries (DELs) to Single-Stranded DELs with Compounds at the 3' End: Its Application in Photo-Crosslinking Selection.

DNA-encoded library (DEL) technology is a crucial tool in pharmaceutical research, rapidly identifying compounds that bind to a target of interest from an extensive pool of compounds. In this study, we propose a new method for generating single-stranded DELs (ssDELs) with compounds at the 3' end. The introduction of uniquely designed hairpin-shaped headpieces containing deoxyuridine (NC-HP) and the use of a cleavage enzyme facilitate the conversion from double-stranded DELs (dsDELs) to such ssDELs. Moreover, Klenow fill-in provides the dsDELs with photo-crosslinkers covalently linked to the coding region, which exhibit durability even under stringent washing conditions and enable photo-crosslinking with a high signal-to-noise ratio, as also confirmed in cell-based photo-crosslinking selections.

Synthesis, Structural, DFT, In Vitro Biological Exploration, and DNA Interaction of New Ni(II) and Cu(II) Mixed‐Ligand Complexes Featuring 2,2′‐Pyridine‐2,6‐diylbis(1H‐benzimidazole) and 2‐Hydroxy‐naphthaldehyde‐Based Schiff‐Base

ABSTRACTThis research article provides a comprehensive analysis of the preparation, structural characterization, 3D modeling, and biological screening of two novel formulated metal complexes, NiPDBZNPH and CuPDBZNPH. These complexes were obtained by combining 2,2′‐pyridine‐2,6‐diylbis(1H‐benzimidazole) (PDBZ) and 2‐hydroxy‐naphthaldehyde Schiff‐base (NPH) ligands with Ni(II) and Cu(II) ions, resulting in the compounds [Ni (NPH)(PDBZ)(Cl)] and [Cu (NPH)(PDBZ)(Cl)], respectively. Spectroscopic and analytical techniques confirmed that both complexes exhibit octahedral geometry around the central metal ion. Computational studies using density functional theory (DFT) corroborated the molecular structures and investigated the quantum chemical properties of the ligands and complexes. A series of in vitro assays were conducted to assess their anti‐inflammatory, antifungal, antibacterial, and antioxidant properties, revealing enhanced bioactivity compared to the uncoordinated ligands. The potential of NiPDBZNPH and CuPDBZNPH as therapeutic candidates was also explored through interactions with calf thymus DNA (CT‐DNA). Molecular docking simulations targeting specific protein receptors were performed to evaluate binding affinities and ligand–protein interactions, with results highlighting significant interactions within enzyme active sites. These findings emphasize the pivotal role of metal–ligand coordination in enhancing pharmacological properties and contribute valuable insights toward the design of novel therapeutic compounds for use in medicinal chemistry and pharmaceutical research.

Deep learning pipeline for accelerating virtual screening in drug discovery

In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce VirtuDockDL, which is a streamlined Python-based web platform utilizing deep learning for drug discovery. This pipeline employs a Graph Neural Network to analyze and predict the effectiveness of various compounds as potential drug candidates. During the validation phase, VirtuDockDL was instrumental in identifying non-covalent inhibitors against the VP35 protein of the Marburg virus, a critical target given the virus’s high fatality rate and limited treatment options. Further, in benchmarking, VirtuDockDL achieved 99% accuracy, an F1 score of 0.992, and an AUC of 0.99 on the HER2 dataset, surpassing DeepChem (89% accuracy) and AutoDock Vina (82% accuracy). Compared to RosettaVS, MzDOCK, and PyRMD, VirtuDockDL outperformed them by combining both ligand- and structure-based screening with deep learning. While RosettaVS excels in accurate docking but lacks high-throughput screening, and PyRMD focuses on ligand-based methods without AI integration, VirtuDockDL offers superior predictive accuracy and full automation for large-scale datasets, making it ideal for comprehensive drug discovery workflows. These results underscore the tool’s capability to identify high-affinity inhibitors accurately across various targets, including the HER2 protein for cancer therapy, TEM-1 beta-lactamase for bacterial infections, and the CYP51 enzyme for fungal infections like Candidiasis. To sum up, VirtuDockDL combines user-friendly interface design with powerful computational capabilities to facilitate rapid, cost-effective drug discovery and development. The integration of AI in drug discovery could potentially transform the landscape of pharmaceutical research, providing faster responses to global health challenges. The VirtuDockDL is available at https://github.com/FatimaNoor74/VirtuDockDL.

Exploring the Role of Herbal Compounds in Skin Aging: ASystematic Review of Topical Approaches.

Recently, dermatology has increasingly focused on understanding skin aging and exploring novel therapeutic approaches. Despite progress in cosmetic and pharmaceutical research, a significant gap remains in comprehensively understanding the effects and mechanisms of herbal extracts on skin aging. While many studies have examined the bioactivities of herbal compounds in preclinical models, comprehensive human trials have been scarce over the past decade. This review aims to address this gap by synthesizing human trials from the past decade, focusing on the therapeutic effects of herbal extracts on skin aging. The objective is to unravel the mechanisms contributing to skin aging and assess the therapeutic potential of herbal compounds. Following the PRISMA 2020 guideline, a systematic review was performed across OvidMEDLINE, Cochrane Central Register of Controlled Trials, and Embase via Ovid. A meticulous search strategy identified relevant clinical trials. The review highlights the essential role of herbal compounds in skin aging, particularly their antioxidant activity in suppressing the aging process. Analysis of 51 clinical trials offers valuable insights into their diverse effects on skin aging parameters. Herbal compounds are promising alternatives to synthetic products for treating skin aging. Their demonstrated efficacy in mitigating wrinkles, enhancing elasticity, maintaining hydration, and controlling pigmentation underscores their potential in developing antiaging therapeutics. However, further studies are needed to identify specific compounds responsible for these effects and understand their mechanisms. Future directions include conducting large-scale trials, exploring synergies with other ingredients, and optimizing delivery systems for sustainable, effective antiaging therapies.

Abstract Book

Proceedings of 2nd International Conference on Modern Tools and Approaches in the Emerging Field of Pharmaceutical and Biomedical Research 20-22, Nov 2024

Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning

In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).

Assessment of the healing properties of Cichorium intybus L. extracts on wounds

BackgroundThroughout the course of an individual's lifespan, they may come across a diverse range of minor or significant incidents that have the potential to result in the development of scars on any region of their physique. These scars may also be a consequence of the body's innate responses to different stimuli. Various medicinal drugs and methods have been used to treat scars. Herbal remedies have long been popular. Cichorium intybus L., or chicory, has been used for centuries to treat numerous illnesses and deformities. PurposeThe primary objective and novelty of this research endeavor involve the investigation of potential therapeutic attributes inherent in extracts derived from different components of C. intybus (common chicory), encompassing the root, stem, flower, and whole herba. This will be accomplished through the utilization of in vitro scratch tests and in vitro cell migration assays. Additionally, the ash acquired from cremation, a method routinely employed by the general population, was also investigated. Study designThe focus of this investigation was to evaluate the wound healing effects of these extracts. The research encompassed a comprehensive examination of several components of the plant in order to ascertain the extracts that exhibited the highest efficacy. MethodsThe study utilized the following approaches respectively to investigate the plant's wound healing abilities: collection and identification of plants, preparation of extracts, determination of phenolic compounds by HPLC-PDA, in vitro cell culture studies, cell viability analysis, wound healing analysis, statistical analysis. ResultsThe findings indicated that the stem extracts of C. intybus, with the exception of the hexane extract (Sample 1), demonstrated notable wound healing properties. According to the chromatography results the main component of extract is chlorogenic acid. The aforementioned findings offer significant contributions to the potential directions for future research within this particular sector. ConclusionThe evidence suggests that extracts from C. intybus have wound healing properties, highlighting its significance in traditional medicine and pharmaceutical research. Further investigation into active chemicals and mechanisms contributing to its wound healing properties is needed, potentially leading to advancements in wound healing therapies.

Reptiles as Experimental Models: Uncovering Their Role in Drug Development and Health Research

Experimental animal models are indispensable as they help to enhance the understanding of unknown diseases and contribute to new discoveries through their physiology, which facilitates the learning process and scientific dissemination. Consequently, the techniques applied to animal models require the development of guidelines that ensure respect for these animals and the reliability of the results obtained, in order to promote the advancement of scientific progress. With this in mind, the aim of the present study was to conduct a review on the use of reptiles as experimental models. The works were reviewed and selected based on their use of animals (reptiles) as experimental models and for having results relevant to the development of science and its various applications. In this context, analyzes that adhered to these principles were also considered, revealing that certain species of snakes, lizards, and turtles show promise for research in pharmaceuticals, the production of medicines and vaccines, as well as in seeking solutions to other problems related to animal health, for example, the study of baseline corticosterone (CORT) concentrations in rattlesnakes. Thus, we can conclude that, although these animals are not as commonly used in experimental research, reptiles hold an important position among vertebrates for investigating the progression of serious diseases, as well as for the development of vaccines and medications.

Biomimetic Folding Strategies for Chemical Synthesis of Disulfide-Bonded Peptides and Proteins.

Disulfide-bonded peptides and proteins, including hormones, toxins, growth factors, and others, are abundant in living organisms. These molecules play crucial physiological roles such as regulating cell and organism growth, development, and metabolism. They have also found widespread applications as drugs or tool molecules in biomedical and pharmaceutical research. However, the chemical synthesis of disulfide-bonded proteins is complicated by the challenges associated with their folding. This review focuses on the latest advancements in disulfide-bonded peptide and protein folding technologies. Particularly, it highlights biomimetic folding strategies that emulate the naturally occurring oxidative folding processes in nature. These strategies include chaperone-assisted folding, glycosylation-assisted folding, and organic-based oxidative folding methods. The review also anticipates future directions in folding technology. Such research offers innovative approaches for the chemical synthesis of complex proteins that are otherwise difficult to fold.