Drug Development Research Articles

Advancing Research and Development of Anti-infectives for Children: A Clinical Development Perspective.

The HIV treatment landscape for adults has progressed dramatically in recent decades; however, paediatric populations continue to experience delayed and limited access to effective and safe antiretroviral therapy options. Despite current incentive programs, formulation research and development and approved drug dosing for children have been limited, particularly for neonates (aged <4 weeks). Regulatory approval of drug formulations and dosing in children may lag behind adult approvals by years. Formulation and trial design adjustments complicate paediatric drug development, all of which are vital to accommodate for physiological differences, organ maturation, and rapid weight gain, which are most significant in the youngest children. To facilitate more rapid anti-infective drug development for paediatric populations, regulatory agencies provide guidelines that include extrapolating efficacy and safety data from relevant populations; using pharmacokinetic (PK) bridging and modelling to reduce sample sizes and limit the number of PK studies needed before efficacy analyses; and enrolling age- or weight-based cohorts in parallel rather than sequentially for clinical trials. Ensuring access to approved drugs poses an additional challenge, as uncertainty in demand leads to manufacturing and supply complexity with potentially higher costs that can be a barrier to uptake. Here we summarize challenges in drug development for children living with HIV, which are not unique to antiretrovirals. We aim to propose strategies for how model-based approaches and global partnerships can overcome some of these barriers to accelerate paediatric drug development, with particular reference to HIV, and how lessons learnt from HIV could be extended to other anti-infectives.

Benchmarking reverse docking through AlphaFold2 human proteome.

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

Tectorigenin Inhibits Glycolysis-induced Cell Growth and Proliferation by Modulating LncRNA CCAT2/miR-145 Pathway in Colorectal Cancer.

Colorectal cancer (CRC) places a heavy burden on global health. Tectorigenin (Tec) is a type of flavonoid-based compound obtained from the Chinese medical herb Leopard Lily Rhizome. It was found to exhibit remarkable anti-tumor properties in previous studies. However, the effect and molecular mechanisms of Tec in colorectal cancer have not been reported. The objective of this study was to explore the action of Tec in proliferation and glycolysis in CRC and the potential mechanism with regard to the long non-coding RNA (lncRNA) CCAT2/micro RNA-145(miR-145) pathway in vitro and in vivo . The anti-tumor effect of Tec in CRC was examined in cell and animal studies, applying Cell Counting Kit-8 (CCK-8) assay as well as xenograft model experiments. Assay kits were utilized to detect glucose consumption and lactate production in the supernatant of cells and animal serum. The expression of the glycolysis-related proteins was assessed by Western Blotting, and levels of lncRNA CCAT2 and miR-145 in CRC tissue specimens and cells were assessed by realtime quantitative PCR (RT-qPCR). Tec significantly suppressed cell glycolysis and proliferative rate in CRC cells. It could decrease lncRNA CCAT2 in CRC cells but increase the expression of miR-145. LncRNA CCAT2 overexpression or inhibition of miR-145 could abolish the inhibitive effects of Tec on the proliferation and glycolysis of CRC cells. The miR-145 mimic rescued the increased cell viability and glycolysis levels caused by lncRNA CCAT2 overexpression. Tec significantly inhibited the growth and glycolysis of CRC xenograft tumor. The expression of lncRNA CCAT2 decreased while the expression of miR-145 increased after Tec treatment in vivo. Tec can inhibit the proliferation and glycolysis of CRC cells through the lncRNA CCAT2/miR-145 axis. Altogether, the potential targets discovered in this research are of great significance for CRC treatment and new drug development.

Emerging Phytochemicals to Treat Leishmaniasis: A Review of Experimental Studies from 2011 to 2021

: Leishmaniasis is a protozoan disease caused by a parasite from the genus Leishmania. It is known as a neglected tropical disease by WHO and is the second-leading cause of death by parasites after malaria. Chemotherapy is the only effective way to control the disease, but treatment options for leishmaniasis are limited. The majority of the drugs are costly, have serious side effects and necessitate hospitalisation. The lack of an effective vaccine, in addition to the emergence of resistance to currently available drugs, has all been raised as major concerns, especially in endemic areas of developing countries. Phytochemicals might contribute to the development of novel and effective drugs for the treatment of leishmaniasis by providing selectively targeted intervention in parasites. Many phytochemicals (quinones, alkaloids, terpenes, saponins, phenolics) and their derivatives are quite active against diverse groups of pathogens, such as viruses, bacteria, fungi and parasites. To date, many phytochemicals have shown potent anti-leishmanial activity with highly selective mode of action. However, due to a lack of interaction between academician and pharma industries none of them has undergone the clinical assessment. The present review will analyse the most promising phytochemicals and their synthetic compounds, which have shown antileishmal activity in in-vitro and subsequently in animal studies from 2011-2021. These phytochemicals are apigenin, hydroxyflavanone, Epigallocatechin-O-3 gallate, caffeic acid, α-bisabolol, β-caryophyllene, ursolic acid, quinones, which have shown notable anti-leishmanial activities in several independent studies.

A Mini-Review on the Structural Characteristics and Anticancer Activity of Nagilactones

Abstract: Natural products provide abundant resources for the development of new drugs. Podocarpus nagi is an arbor of Podocarpus L'Hér. ex Persoon. Its fruits, leaves, and roots exhibit a broad spectrum of pharmacological activities (such as antitumor, plant growth regulation, termite killing, and insect larval toxicity), which have been used in Yao folk for a long history. Nagilactone is one of the key components discovered in Podocarpus nagi, which has a variety of structures and a broad spectrum of antitumor activities. In this mini-review, the structures and spectral characteristics, together with the antitumor activities and the structure-activity relationships of nagilactones, are summarized by searching the database in order to provide detailed references for researchers to elucidate and modify their structures.

Cracking the chaperone code through the computational microscope.

The heat shock protein 90 kDa (Hsp90) chaperone machinery plays a crucial role in maintaining cellular homeostasis. Beyond its traditional role in protein folding, Hsp90 is integral to key pathways influencing cellular function in health and disease. Hsp90 operates through the modular assembly of large multiprotein complexes, with their composition, stability, and localization adapting to the cell's needs. Its functional dynamics are finely tuned by ligand binding and post-translational modifications (PTMs). Here, we discuss how to disentangle the intricacies of the complex code that governs the crosstalk between dynamics, binding, PTMs, and the functions of the Hsp90 machinery using computer-based approaches. Specifically, we outline the contributions of computational and theoretical methods to the understanding of Hsp90 functions, ranging from providing atomic-level insights into its dynamics to clarifying the mechanisms of interactions with protein clients, cochaperones, and ligands. The knowledge generated in this framework can be actionable for the design and development of chemical tools and drugs targeting Hsp90 in specific disease-associated cellular contexts. Finally, we provide our perspective on how computation can be integrated into the study of the fine-tuning of functions in the highly complex Hsp90 landscape, complementing experimental methods for a comprehensive understanding of this important chaperone system.

Research on the Regulatory Mechanism of Ginseng on the Tumor Microenvironment of Colorectal Cancer based on Network Pharmacology and Bioinformatics Validation.

A network pharmacology study on the biological action of ginseng in the treatment of colorectal cancer (CRC) by regulating the tumor microenvironment (TME). To investigate the potential mechanism of action of ginseng in the treatment of CRC by regulating TME. This research employed network pharmacology, molecular docking techniques, and bioinformatics validation. Firstly, the active ingredients and the corresponding targets of ginseng were retrieved using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the Traditional Chinese Medicine Integrated Database (TCMID), and the Traditional Chinese Medicine Database@Taiwan (TCM Database@Taiwan). Secondly, the targets related to CRC were retrieved using Genecards, Therapeutic Target Database (TTD), and Online Mendelian Inheritance in Man (OMIM). Tertiary, the targets related to TME were derived from screening the GeneCards and National Center for Biotechnology Information (NCBI)-Gene. Then the common targets of ginseng, CRC, and TME were obtained by Venn diagram. Afterward, the Protein-protein interaction (PPI) network was constructed in the STRING 11.5 database, intersecting targets identified by PPI analysis were introduced into Cytoscape 3.8.2 software cytoHubba plugin, and the final determination of core targets was based on degree value. The OmicShare Tools platform was used to analyze the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the core targets. Autodock and PyMOL were used for molecular docking verification and visual data analysis of docking results. Finally, we verified the core targets by Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases in bioinformatics. A total of 22 active ingredients and 202 targets were identified to be closely related to the TME of CRC. PPI network mapping identified SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 as possible core targets. Go enrichment analysis showed that it was mainly involved in T cell co-stimulation, lymphocyte co-stimulation, growth hormone response, protein input, and other biological processes; KEGG pathway analysis found 123 related signal pathways, including EGFR tyrosine kinase inhibitor resistance, chemokine signaling pathway, VEGF signaling pathway, ErbB signaling pathway, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. The molecular docking results showed that the main chemical components of ginseng have a stable binding activity to the core targets. The results of the GEPIA database showed that the mRNA levels of PIK3R1 were significantly lowly expressed and HSP90AA1 was significantly highly expressed in CRC tissues. Analysis of the relationship between core target mRNA levels and the pathological stage of CRC showed that the levels of SRC changed significantly with the pathological stage. The HPA database results showed that the expression levels of SRC were increased in CRC tissues, while the expression of STAT3, PIK3R1, HSP90AA1, and AKT1 were decreased in CRC tissues. Ginseng may act on SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 to regulate T cell costimulation, lymphocyte costimulation, growth hormone response, protein input as a molecular mechanism regulating TME for CRC. It reflects the multi-target and multi-pathway role of ginseng in modulating TME for CRC, which provides new ideas to further reveal its pharmacological basis, mechanism of action and new drug design and development.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling.

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Global impact of antimicrobial resistance on cancer care: Challenges and strategies in low- and middle-income countries

Antimicrobial resistance (AMR) is a fast-rising global health warning that causes significant challenges to the treatment and management of infectious diseases, especially in cancer patients, who are a large number of the vulnerable populations. This work underlines the complicated problems faced by the low- and middle-income countries (LMICs) in cancer care due to AMR. The burden of both cancer and AMR is sadly high, so therefore this review sheds light into the possible potential strategies to win against these challenges and set up better cancer treatment for improved outcomes in these countries. This sheds more light and gives a deeper understanding of recent research works, epidemiological data, and relevant case studies. Furthermore, examining the interconnectivity between AMR and cancer care, narrowing it down to some particular challenges and vulnerabilities encountered by cancer patients in LMICs. The study identifies key strategies such as implementing better antimicrobial stewardship programs, improving infection prevention and control, promoting new drug development, and enhancing global interactions as important steps in reducing the effect of AMR on cancer care in LMICs. This emphasizes the urgent need for targeted interventions, context-specific solutions, building sustainable development, and global interactions to address the complex challenges posed by AMR in cancer care within LMICs.

AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics in Biopharmaceuticals

Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential.

Framework for Multistakeholder Patient Registries in the Field of Rare Diseases: Focus on Neurogenetic Diseases.

Progress in genetic diagnosis and orphan drug legislation has opened doors to new therapies in rare neurogenetic diseases (RNDs). Innovative therapies such as gene therapy can improve patients' quality of life but come with academic, regulatory, and financial challenges. Registries can play a pivotal role in generating evidence to tackle these, but their development requires multidisciplinary knowledge and expertise. This study aims to develop a practical framework for creating and implementing patient registries addressing common challenges and maximizing their impact on care, research, drug development, and regulatory decision making with a focus on RNDs. A comprehensive 3-step literature and qualitative research approach was used to develop the framework. A qualitative systematic literature review was conducted, extracting guidance and practices leading to the draft framework. Subsequently, we interviewed representatives of 5 established international RND registries to add learnings from hands-on experiences to the framework. Expert input on the draft framework was sought in digital multistakeholder focus groups to refine the framework. The literature search; interviews with 5 registries; and focus groups with patient representatives (n = 4), clinicians (n = 6), regulators, health technology assessment (HTA) bodies and payers (n = 7), industry representatives (n = 7), and data/information technology (IT) specialists (n = 5) informed development of the framework. It covers the interests of different stakeholders, purposes for data utilization, data aspects, IT infrastructure, governance, and financing of rare disease registries. Key principles include that data should be rapidly accessible, independent, and trustworthy. Governance should involve multiple stakeholders. In addition, data should be highly descriptive, machine-readable, and accessible through a shared infrastructure and not spread over multiple isolated repositories. Sustainable and independent financing of registries is deemed important but remains challenging because of a lack of widely supported funding models. The proposed framework will guide stakeholders in establishing or improving rare disease registries that fulfill requirements of academics and patients as well as regulators, HTA bodies, and commercial parties. There is a need for more clarity regarding quality requirements for registries in regulatory and HTA context. In addition, independent financing models for registries should be developed, as well as well-defined policies on technical uniformity in health data.

Expedited program and utilization for anticancer drug approval in China and the United States

Objective: To systematically summarize and comparatively analyze the development, establishment and usage of oncology drugs speedy review approaches in China and in the United States between 2012 and 2021. Methods: Based on National Medical Products Administration (NMPA) and Food and Drug Administration (FDA) websites, the development and current status of the speedy review approaches were consulted and summarized. Approved oncology drugs in China and in the United States (87 in China, 118 in the United States) over the past decade were analyzed using chi-square test for group comparison. Results: Five speedy approaches have been established in China and in the United States, three of which are the same, priority review, conditional approval or accelerated approval and breakthrough therapy. The rest two are special review and approval, special examination and approval in China, and fast track and real-time oncology review in the United States. Compared to the United States, speedy review approaches in China set up late (1992 vs. 2005). The overall utilization rates of the oncology drugs speedy review approaches were similar between the China and United States (90.8% vs. 92.4%, P=0.800) in the previous 10 years, and priority review have highest utilization rates in both China and the United States without significant group difference (77.0% vs. 82.2%, P=0.381); relatively low utilization rates of conditional approval (31.0% vs. 44.9%, P=0.041) and breakthrough therapy (2.3% vs. 50.0%, P＜0.001) were seen in China. 52.9% of new drugs applied for special examination and approval in China and 40.7% of new drugs applied for fast track in the United States. Overall, the priority review both in China and the United States are stable, with a similar average annual utilization rate (84.8% vs. 83.7%); accelerated approval and breakthrough therapies in the United States fluctuate wildly, but the situation is tending towards stability in the last 3 years. Conclusions: Both China and the United States have established a relatively complete accelerated review system, with an overall utilization rate over 90%; China's accelerated review started late, although the overall utilization rate is close to that of the United States. The utilization rates of conditional approval and breakthrough therapy are still relatively low. Flexible usage of speedy review approaches, gaining regulatory recognition to use alternative endpoints, achieving real-time review and guidance are keys to accelerate new drug development in China.

Abstract C054: Current anticancer drugs differentially impact Black and White prostate cancer cell lines

Abstract Introduction/Background: In pre-clinical models of prostate cancer (PCa) such as the utilization of cell lines, it is important to address the limited diversity present. This acknowledgment is vital to facilitate equitable assessment of the efficacy and safety of drug development across various races and ethnicities. Of all commercially available PCa cell lines, 98% of them are of European origin. The genetic and biological disparities between PCa cell lines derived from men of European ancestry (MEA) and men of African ancestry (MAA) may underlie the observed enhanced responsiveness of tumours to existing anticancer agents in MEA compared to MAA. This difference is notable given that MAA are 2.5 times more likely to succumb to PCa. This study compared the cytotoxic effects of five anticancer drugs (Docetaxel, Cabazitaxel, Abiraterone, Olaparib, Enzalutamide) and the natural product betaine on MAA (ACRJ-PC28, MDA-PCA-2b) and MEA (DU-145, PC-3) PCa cell lines. Method: After a 72-hour treatment with the anti-cancer drugs and the natural product, the inhibitory effects of these anti-cancer agents on the four cell lines were investigated using three different assays. CellTiter Aqueous, Neutral Red, and Crystal Violet assays were utilized due to distinct growth patterns observed in the various cell lines. By comparing the optical density (OD) readings and examining the cellular morphology through microscopic visualization, we aimed to determine which assay(s) yielded the most reliable results in determining accurate IC50 values. Results: Drug selectivity was observed for Abiraterone and Olaparib that were most effective in ACRJ-PC28 (IC50 = 1.10 μM) and PC-3 (IC50 = 0.28 μM) cells compared to the other PCa cell lines. Enzalutamide differentially impacted MAA, (IC50= 206 μM for ACRJ-PC28 and 104 μM for MDA-PCA-2b) and MEA (IC50 = 37 μM for PC-3 and 48 μM for DU-145) PCa cell lines. However, no distinct impact was observed for Docetaxel (IC50s = 0.05 nM for ACRJ-PC28; 0.08 nM for MDA-PCA-2b, 0.07 nM for PC-3 and 0.08 nM for DU-145) and Cabazitaxel ((IC50s = 0.56 nM for ACRJ-PC28; 0.45 nM for MDA-PCA-2b, 0.43 nM for PC-3 and 0.38 nM for DU-145) as both drugs impacted the cell lines very potently in the nM range. Conclusion: Due to the heterogenous nature of cancers, especially in PCa, a single drug or drug class may not be the best course of treatment. Instead, a combination of drugs with different mechanisms may be more suitable for addressing the diversity in cancer types. This points to the evolving understanding of personalized cancer treatment, where a strategic integration of first, second, and third-line therapies can effectively address the genetic and epigenetic factors that are unique to individuals in cancer management. Citation Format: Simone Badal, Bor-Jang Hwang, Ashlianne Nelson, Kristoff Frank, Rory Thompson, Belinda Morison, Valerie Marah, Camille Ragin. Current anticancer drugs differentially impact Black and White prostate cancer cell lines [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C054.

Artificial intelligence as a tool in drug discovery and development

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

The Application of Bicyclo[1.1.1]pentane as a Bioisostere of the Phenyl Ring in Pharmaceutical Chemistry

AbstractBicyclo[1.1.1]pentane (BCP) is a bridging ring skeleton with three-dimensional structure. BCP is a bioisostere of the phenyl ring, tert-butyl group, and alkynes; it has excellent physical and chemical properties compared with phenyl ring, so it has been widely considered by the pharmaceutical chemistry. This short review examines related reports of BCP as a bioisostere of the phenyl ring, and the changes in physicochemical properties and biological activity after substitution. The solubility, clogP, and metabolic toxicity of the drug are improved by BCP bioisosteres, but the biological activity of BCP is lower than the phenyl ring. The application of BCP in drug research and development will be further expanded to provide more possibilities for future drug innovation and development.1 Introduction2 Replacement of Phenyl Rings by BCP3 Conclusion

Identification of New Angiotensin-Converting Enzyme Inhibitory Peptides Isolated from the Hydrolysate of the Venom of Nemopilema nomurai Jellyfish

Recently, jellyfish venom has gained attention as a promising reservoir of pharmacologically active compounds, with potential applications in new drug development. In this investigation, novel peptides, isolated from the hydrolysates of Nemopilema nomurai jellyfish venom (NnV), demonstrate potent inhibitory activities against angiotensin-converting enzyme (ACE). Proteolytic enzymes—specifically, papain and protamex—were utilized for the hydrolysis under optimized enzymatic conditions, determined by assessing the degree of hydrolysis through the ninhydrin test. Comparative analyses revealed that papain treatment exhibited a notably higher degree of NnV hydrolysis compared to protamex treatment. ACE inhibitory activity was quantified using ACE kit-WST, indicating a substantial inhibitory effect of 76.31% for the papain-digested NnV crude hydrolysate, which was validated by captopril as a positive control. The separation of the NnV-hydrolysate using DEAE sepharose weak-anion-exchange chromatography revealed nine peaks under a 0–1 M NaCl stepwise gradient, with peak no. 3 displaying the highest ACE inhibition of 96%. The further purification of peak no. 3 through ODS-C18 column reverse-phase high-performance liquid chromatography resulted in five sub-peaks (3.1, 3.2, 3.3, 3.4, and 3.5), among which 3.2 exhibited the most significant inhibitory activity of 95.74%. The subsequent analysis of the active peak (3.2) using MALDI–TOF/MS identified two peptides with distinct molecular weights of 896.48 and 1227.651. The peptide sequence determined by MS/MS analysis revealed them as IVGRPLANG and IGDEPRHQYL. The docking studies of the two ACE-inhibitory peptides for ACE molecule demonstrated a binding affinity of −51.4 ± 2.5 and −62.3 ± 3.3 using the HADDOCK scoring function.

Targeted therapies, novel antibodies, and immunotherapies in advanced non-small cell lung cancer: clinical evidence and drug approval patterns.

Since 2011, the U.S. FDA has approved 30 new drugs for use in advanced non-small-cell lung cancer (NSCLC), mainly comprising tyrosine kinase inhibitors and immune checkpoint inhibitors. NSCLC with oncogene driver alterations is amenable to treatment with targeted drugs, usually small molecule inhibitors. In these cases, the demonstration of high overall response rates, coupled with a lasting duration of response, has allowed for accelerated approval in the U.S., based on single or multi-cohort trials. Confirmatory clinical evidence was subsequently provided through post-marketing trials. In NSCLC without such driver alterations, regulatory agencies in both the U.S. and the EU set clinical evidence expectations that foster the conduct of studies primarily focused on determining survival or event-free survival, based on randomized-controlled trial designs. This review analyzes the approval patterns of novel therapeutics for NSCLC with a focus on small molecule inhibitors that target driver alterations, as well as biologicals. The latter include monoclonal antibodies inhibiting immune checkpoints like PD-(L)1 or cell surface receptors, and antibody-drug conjugates, highly potent biologics linked to a cytotoxic compound. The differentiation of NSCLC into oncogene- and non-oncogene-addicted subtypes determines drug development strategies, the extent of the clinical development program, access to orphan drug development incentives, and regulatory approval strategies.

Antimalarial Mechanisms and Resistance Status of Artemisinin and Its Derivatives

Artemisinin is an endoperoxide sesquiterpene lactone isolated from Artemisia annua and is often used to treat malaria. Artemisinin’s peroxide bridge is the key structure behind its antimalarial action. Scientists have created dihydroartemisinin, artemether, artesunate, and other derivatives preserving artemisinin’s peroxide bridge to increase its clinical utility value. Artemisinin compounds exhibit excellent efficacy, quick action, and minimal toxicity in malaria treatment and have greatly contributed to malaria control. With the wide and unreasonable application of artemisinin-based medicines, malaria parasites have developed artemisinin resistance, making malaria prevention and control increasingly challenging. Artemisinin-resistant Plasmodium strains have been found in many countries and regions. The mechanisms of antimalarials and artemisinin resistance are not well understood, making malaria prevention and control a serious challenge. Understanding the antimalarial and resistance mechanisms of artemisinin drugs helps develop novel antimalarials and guides the rational application of antimalarials to avoid the spread of resistance, which is conducive to malaria control and elimination efforts. This review will discuss the antimalarial mechanisms and resistance status of artemisinin and its derivatives, which will provide a reference for avoiding drug resistance and the research and development of new antimalarial drugs.

Multi-output prediction of dose–response curves enables drug repositioning and biomarker discovery

Drug response prediction is hampered by uncertainty in the measures of response and selection of doses. In this study, we propose a probabilistic multi-output model to simultaneously predict all dose–responses and uncover their biomarkers. By describing the relationship between genomic features and chemical properties to every response at every dose, our multi-output Gaussian Process (MOGP) models enable assessment of drug efficacy using any dose–response metric. This approach was tested across two drug screening studies and ten cancer types. Kullback-leibler divergence measured the importance of each feature and identified EZH2 gene as a novel biomarker of BRAF inhibitor response. We demonstrate the effectiveness of our MOGP models in accurately predicting dose–responses in different cancer types and when there is a limited number of drug screening experiments for training. Our findings highlight the potential of MOGP models in enhancing drug development pipelines by reducing data requirements and improving precision in dose–response predictions.

Alzheimer’s Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2–7 Binding Affinity

Alzheimer’s Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2–7 Binding Affinity