Principles of early drug discovery

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Novel dihydropyrimidines as promising EGFR & HER2 inhibitors: Insights from experimental and computational studies

Dihydropyrimidines are widely recognized for their diverse biological properties and are often synthesized by the Biginelli reactions. In this backdrop, a novel series of Biginelli dihydropyrimidines were designed, synthesized, purified, and analyzed by FT-IR, 1H NMR, 13C NMR, and mass spectrometry. Anticancer activity against MCF-7 breast cancer cells was evaluated as part of their cytotoxicity in comparison with the normal Vero cells. The cytotoxicity of dihydropyrimidines ranges from moderate to significant. Among the 38 dihydropyrimidines screened, compounds 16, 21, and 39 exhibited significant cytotoxicity. These 3 compounds were subjected to flow cytometry studies and EGFRwt Kinase inhibition assay using lapatinib as a standard. The study included evaluation for the inhibition of EGFR and HER2 expression at five different concentrations. At a concentration of 1000 nM compound 21 showed 98.51 % and 96.79 % inhibition of EGFR and HER2 expression. Moreover, compounds 16, 21 and 39 significantly inhibited EGFRwt activity with IC50 = 69.83, 37.21 and 76.79 nM, respectively. In addition, 3D-QSAR experiments were conducted to elucidate Structure activity relationships in a 3D grid space by comparing the experimental and predicted cytotoxic activities. Molecular docking studies were performed to validate the results by in silico method. All together, we developed a new series of Biginelli dihydropyrimidines as dual EGFR/HER2 inhibitors.

Seven-membered N-heterocycles as approved drugs and promising leads in medicinal chemistry as well as the metal-free domino access to their scaffolds

Azepanes or azepines are structural motifs of many drugs, drug candidates and evaluated lead compounds. Even though compounds having N-heterocyclic 7-membered rings are often found in nature (e.g. alkaloids), the natural compounds of this group are rather rare as approved therapeutics. Thus, recently studied and approved azepane or azepine-congeners predominantly consist of semi-synthetically or synthetically-obtained scaffolds. In this review a comparison of approved drugs and recently investigated leads was proposed taking into regard their structural aspects (stereochemistry), biological activities, pharmacokinetic properties and confirmed molecular targets. The 7-membered N-heterocycles reveal a wide range of biological activities, not only against CNS diseases, but also as e.g. antibacterial, anticancer, antiviral, antiparasitic and against allergy agents. As most of the approved or investigated potential drugs or lead structures, belonging to 7-membered N-heterocycles, are synthetic scaffolds, this report also reveals different and efficient metal-free cascade approaches useful to synthesize both simple azepane or azepine-containing congeners and those of oligocyclic structures. Stereochemistry of azepane/azepine fused systems, in view of biological data and binding with the targets, is discussed. Apart from the approved drugs, we compare advances in SAR studies of 7-membered N-heterocycles (mainly from 2018 to 2023), whereas the related synthetic part concerning various domino strategies is focused on the last ten years.

How does a Saccharomyces cerevisiae extract influence the components of isolated rotavirus particles from stool samples collected in a clinical setting from children?

Human Rotavirus (HRV) is the causative pathogen of severe acute enteric infections that cause mortality among children worldwide. This study focuses on developing a new and effective treatment for rotavirus infection using an extract from Saccharomyces cerevisiae, aiming to make this treatment easily accessible to everyone. 15 antigens and 26 antibodies were detected in serum and stool using ELISA. The titers of HRVq1, HRVq2, HRVC1, and HRVC2 on Vero cells were determined to be 1.2x106, 3.0x106, 4.2x106, and 7.5x105 (Plaque forming unit, PFU/ml) four days after infection, respectively. The HRVq1 isolate induced cytopathic effects, i.e., forming multinucleated, rounded, enlarged, and expanding gigantic cells. RT-PCR identified this isolate, and the accession number 2691714 was assigned to GeneBank. The molecular docking analysis revealed that nonstructural proteins (NSPs) NSP1, NSP2, NSP3, NSP4, NSP5, and NSP6 exhibited significant binding with RNA. NSP2 demonstrated the highest binding affinity and the lowest binding energy (-8.9 kcal/mol). This affinity was maintained via hydrophobic interactions and hydrogen bonds spanning in length from 1.12 Å to 3.11 Å. The ADMET and bioactivity predictions indicated that the yeast extract possessed ideal solubility, was nontoxic, and did not cause cancer. The inhibitory constant values predicted for the S. cerevisiae extract in the presence of HRV vital proteins varied from 5.32 to 7.45 mM, indicating its potential as a viable drug candidate. Saccharomyces cerevisiae extract could be utilized as a dietary supplement to combat HRV as an alternative dietary supplement.

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.

Zinc(II) coordination compound with N'-(pyridin-2-ylmethylene)nicotinohydrazide: Synthesis, crystal structure, computational and cytotoxicity studies

In this work, we report on the synthesis of a novel zinc(II) coordination compound [ZnL2] (1), which was readily obtained from the reaction of Zn(OAc)·2H2O and N'-(pyridin-2-ylmethylene)nicotinohydrazide (HL) in methanol. Recrystallization of 1 from dimethylformamide under ambient conditions allowed to produce yellow block-like crystals of 1·H2O. Complex 1·H2O was characterized by FT-IR and 1H NMR spectroscopy, while its optical properties were studied by UV-vis and spectrofluorimetry in methanol. The crystal structure of the title complex was revealed by single crystal X-ray diffraction and further explored in detail by the Hirshfeld surface analysis. Theoretical investigations based on the DFT calculations have also been applied to show the electronic properties of complex 1. The antitumor activities of the parent ligand HL and complex 1 were studied using Dalton's lymphoma malignant cancer model. Both compounds were found to induce concentration-dependent cytotoxicity and apoptotic cell death, leading to a decrease in cell viability, body weight, and tumor volume in mice with the superior activity of complex 1 over HL. Mice treated with complex 1 demonstrated an increase in life span with a survival period of 23 days. Finally, using a molecular docking approach, we have probed complex 1 to inhibit the recombinant mouse tumor-necrosis factor alpha (mTNF).

Quantitative Hydrogen-Deuterium Exchange Mass Spectrometry for Simultaneous Structural Characterization and Affinity Indexing of Single Target Drug Candidate Libraries

Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients (KD) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict KD from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.

Structure-Based Drug Design with a Deep Hierarchical Generative Model

Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive and diffusion-based methods in both speed and performance on common generative benchmarks. DrugHIVE's hierarchical design enables improved control over molecular generation. Its capabilities include dramatically increasing virtual screening efficiency and accelerating a wide range of common drug design tasks, including de novo generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Our highly scalable method can even be applied to receptors with high-confidence AlphaFold-predicted structures, extending the ability to generate high-quality drug-like molecules to a majority of the unsolved human proteome.

Natural flavonoids as promising lactate dehydrogenase A inhibitors: Comprehensive in vitro and in silico analysis

The inhibitory potential of 17 flavonoids on lactate dehydrogenase A (LDHA), a key enzyme in the downstream process of aerobic glycolysis in cancer cells, is investigated. Fisetin exhibited excellent inhibitory activity (IC50 = 0.066 µM). Quercetin 3-β-D-glucoside, quercetin 3-galactoside, luteolin, neoeriocitrin, and luteolin 7-O-β-D-glucoside showed good inhibitory activity (IC50 = 1.397-15.730 µM). Biochanin A, baicalein, quercetin, scutellarein-7-glucuronide, diosmetin, baicalein 7-O-β-D-glucuronide, and apigenin 7-apioglucoside demonstrated moderate inhibitory activity (IC50 = 33.007-86.643 µM). Eriodictyol, quercetin 7-O-β-D-glucoside, apigenin 7-O-β-D-glucoside, and epicatechin were inactive. The Lineweaver-Burk plot showed that fisetin competitively inhibits NADH binding (Ki = 0.024 µM). Ki values for other compounds were calculated using the Cheng-Prusoff equation (Ki = 0.2799-2.1661 µM). The study revealed that the inhibitory effect of flavonoids varies with the number and position of OH groups and bound sugars. Molecular docking analyses indicated that flavonoids exhibited strong interactions with the NADH binding site of LDHA through hydrophobic interactions and hydrogen bonds. Molecular dynamic simulations tested the stability of the fisetin-LDHA complex over 100 ns and showed fisetin's high binding affinity to LDHA, maintaining strong hydrogen bonds. The binding energy of fisetin with LDHA was -33.928 kcal/mol, indicating its effectiveness as an LDHA inhibitor. Consequently, flavonoids identified as strong inhibitors could be potential cancer treatment sources through LDHA inhibition.

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.

Machine learning in preclinical drug discovery

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.

Pancreatic cancer organoid-screening captures personalized sensitivity and chemoresistance suppression upon cytochrome P450 3A5-targeted inhibition

Cytochrome P450 3A5 (CYP3A5) has been proposed as a predictor of therapy response in subtypes of pancreatic ductal adenocarcinoma cancer (PDAC). To validate CYP3A5 as a therapeutic target, we developed a high-content image organoid-based screen to quantify the phenotypic responses to the selective inhibition of CYP3A5 enzymatic activity by clobetasol propionate (CBZ), using a cohort of PDAC-derived organoids (PDACOs). The chemoresistance of PDACOs to a panel of standard-of-care drugs, alone or in combination with CBZ, was investigated. PDACO pharmaco-profiling revealed CBZ to have anti-cancer activity that was dependent on the CYP3A5 level. In addition, CBZ restored chemo-vulnerability to cisplatin in a subset of PDACOs. A correlative proteomic analysis established that CBZ caused the suppression of multiple cancer pathways sustained by or associated with a mutant form of p53. Limiting the active pool of CYP3A5 enables targeted and personalized therapy to suppress pro-oncogenic mechanisms that fuel chemoresistance in some PDAC tumors.

Engineered Branaplam Aptamers Exploit Structural Elements from Natural Riboswitches

Drug candidates that fail in clinical trials for efficacy reasons might still have favorable safety and bioavailability characteristics that could be exploited. A failed drug candidate could be repurposed if a receptor, such as an aptamer, were created that binds the compound with high specificity. Branaplam is a small molecule that was previously in development to treat spinal muscular atrophy and Huntington's disease. Here, we report the development of a small (48-nucleotide) RNA aptamer for branaplam with a dissociation constant of ∼150 nM. Starting with a combinatorial RNA pool integrating the secondary and tertiary structural scaffold of a Guanine-I riboswitch aptamer interspersed with regions of random sequence, in vitro selection yielded aptamer candidates for branaplam. Reselection and rational design were employed to improve binding of a representative branaplam aptamer candidate. A resulting variant retains the pseudoknot and two of the paired elements (P2 and P3) from the scaffold but lacks the enclosing paired element (P1) that is essential for the function of the natural Guanine-I riboswitch aptamer. A second combinatorial RNA pool based on the scaffold for TPP (thiamin pyrophosphate) riboswitches also yielded a candidate offering additional opportunities for branaplam aptamer development.

Beyond Cytotoxic Potency: Disposition Features Required to Design ADC Payload

Antibody-drug conjugates (ADCs) have demonstrated impressive clinical usefulness in treating several types of cancer, with the notion of widening of the therapeutic index of the cytotoxic payload through the minimization of the systemic toxicity. Therefore, choosing the most appropriate payload molecule is a particularly important part of the early design phase of ADC development, especially given the highly competitive environment ADCs find themselves in today. The focus of the current review is to describe critical attributes/considerations needed in the discovery and ultimately development of cytotoxic payloads in support of ADC design. In addition to potency, several key dispositional characteristics including solubility, permeability and bystander effect, pharmacokinetics, metabolism, and drug-drug interactions, are described as being an integral part of the integrated activities required in the design of clinically safe and useful ADC therapeutic agents.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

A practical guide to the discovery of biomolecules with biostimulant activity

The growing demand for sustainable solutions in agriculture, which are critical for crop productivity and food quality in the face of climate change and the need to reduce agrochemical usage, has brought biostimulants into the spotlight as valuable tools for regenerative agriculture. With their diverse biological activities, biostimulants can contribute to crop growth, nutrient use efficiency, and abiotic stress resilience, as well as to the restoration of soil health. Biomolecules include humic substances, protein lysates, phenolics, and carbohydrates have undergone thorough investigation because of their demonstrated biostimulant activities. Here, we review the process of the discovery and development of extract-based biostimulants, and propose a practical step-by-step pipeline that starts with initial identification of biomolecules, followed by extraction and isolation, determination of bioactivity, identification of active compound(s), elucidation of mechanisms, formulation, and assessment of effectiveness. The different steps generate a roadmap that aims to expedite the transfer of interdisciplinary knowledge from laboratory-scale studies to pilot-scale production in practical scenarios that are aligned with the prevailing regulatory frameworks.

Site-specific thrombus formation: advancements in photothrombosis-on-a-chip technology

Thrombosis, characterized by blood clot formation within vessels, poses a significant medical challenge. Despite extensive research, the development of effective thrombosis therapies is hindered by substantial costs, lengthy development times, and high failure rates in medication commercialization. Conventional pre-clinical models often oversimplify cardiovascular disease, leading to a disparity between experimental results and human physiological responses. In response, we have engineered a photothrombosis-on-a-chip system. This microfluidic model integrates human endothelium, human whole blood, and blood flow dynamics and employs the photothrombotic method. It enables precise, site-specific thrombus induction through controlled laser irradiation, effectively mimicking both normal and thrombotic physiological conditions on a single chip. Additionally, the system allows for the fine-tuning of thrombus occlusion levels via laser parameter adjustments, offering a flexible thrombus model with varying degrees of obstruction. Additionally, the formation and progression of thrombosis noted on the chip closely resemble the thrombotic conditions observed in mice in previous studies. In the experiments, we perfused recalcified whole blood with Rose Bengal into an endothelialized microchannel and initiated photothrombosis using green laser irradiation. Various imaging methods verified the model's ability to precisely control thrombus formation and occlusion levels. The effectiveness of clinical drugs, including heparin and rt-PA, was assessed, confirming the chip's potential in drug screening applications. In summary, the photothrombosis-on-a-chip system significantly advances human thrombosis modeling. Its precise control over thrombus formation, flexibility in the thrombus severity levels, and capability to simulate dual physiological states on a single platform make it an invaluable tool for targeted drug testing, furthering the development of organ-on-a-chip drug screening techniques.

Computational Exploration of Potential Pharmacological Inhibitors Targeting the Envelope Protein of the Kyasanur Forest Disease Virus

The limitations of the current vaccination strategy for the Kyasanur Forest Disease virus (KFDV) underscore the critical need for effective antiviral treatments, highlighting the crucial importance of exploring novel therapeutic approaches through in silico drug design. Kyasanur Forest Disease, caused by KFDV, is a tick-borne disease with a mortality of 3-5% and an annual incidence of 400 to 500 cases. In the early stage of infection, the envelope protein plays a crucial role by facilitating host-virus interactions. The objective of this research is to develop effective antivirals targeting the envelope protein to disrupt the virus-host interaction. In line with this, the 3D structure of the envelope protein was modeled and refined through molecular modeling techniques, and subsequently, ligands were designed via de novo design and pharmacophore screening, yielding 12 potential hits followed by ADMET analysis. The top five candidates underwent geometry optimization and molecular docking. Notably, compounds L4 (SA28) and L3 (CNP0247967) are predicted to have significant binding affinities of -8.91 and -7.58 kcal/mol, respectively, toward the envelope protein, based on computational models. Both compounds demonstrated stability during 200 ns molecular dynamics simulations, and the MM-GBSA binding free-energy values were -85.26 ± 4.63 kcal/mol and -66.60 ± 2.92 kcal/mol for the envelope protein L3 and L4 complexes, respectively. Based on the computational prediction, it is suggested that both compounds have potential as drug candidates for controlling host-virus interactions by targeting the envelope protein. Further validation through in-vitro assays would complement the findings of the present in silico investigations.

Advancing Point-of-Care Applications with Droplet Microfluidics: From Single-Cell to Multicellular Analysis

Recent advances in single-cell and multicellular microfluidics technology have provided powerful tools for studying cancer biology and immunology. The ability to create controlled microenvironments, perform high-throughput screenings, and monitor cellular interactions at the single-cell level has significantly advanced our understanding of tumor biology and immune responses. We discuss cutting-edge multicellular and single-cell microfluidic technologies and methodologies utilized to investigate cancer-immune cell interactions and assess the effectiveness of immunotherapies. We explore the advantages and limitations of the wide range of 3D spheroid and single-cell microfluidic models recently developed, highlighting the various approaches in device generation and applications in immunotherapy screening for potential opportunities for point-of-care approaches.

Caspases in Alzheimer's Disease: Mechanism of Activation, Role, and Potential Treatment

With the aging of the population, treatment of conditions emerging in old age, such as neurodegenerative disorders, has become a major medical challenge. Of these, Alzheimer's disease, leading to cognitive dysfunction, is of particular interest. Neuronal loss plays an important role in the pathophysiology of this condition, and over the years, a great effort has been made to determine the role of various factors in this process. Unfortunately, until now, the exact pathomechanism of this condition remains unknown. However, the most popular theories associate AD with abnormalities in the Tau and β-amyloid (Aβ) proteins, which lead to their deposition and result in neuronal death. Neurons, like all cells, die in a variety of ways, among which pyroptosis, apoptosis, and necroptosis are associated with the activation of various caspases. It is worth mentioning that Tau and Aβ proteins are considered to be one of the caspase activators, leading to cell death. Moreover, the protease activity of caspases influences both of the previously mentioned proteins, Tau and Aβ, converting them into more toxic derivatives. Due to the variety of ways caspases impact the development of AD, drugs targeting caspases could potentially be useful in the treatment of this condition. Therefore, there is a constant need to search for novel caspase inhibitors and evaluate them in preclinical and clinical trials.

A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications

The manufacturability assessment and optimization of bispecific antibodies (bsAbs) during the discovery stage are crucial for the success of the drug development process, impacting the speed and cost of advancing such therapeutics to the Investigational New Drug (IND) stage and ultimately to the market. The complexity of bsAbs creates challenges in employing effective evaluation methods to detect developability risks in early discovery stage, and poses difficulties in identifying the root causes and implementing subsequent engineering solutions. This study presents a case of engineering a bsAb that displayed a normal solution appearance during the discovery phase but underwent significant precipitation when subjected to agitation stress during 15 L Chemistry, Manufacturing, and Control (CMC) production Leveraging analytical tools, structural analysis, in silico prediction, and wet-lab validations, the key molecular origins responsible for the observed precipitation were identified and addressed. Sequence engineering to reduce protein surface hydrophobicity and enhance conformational stability proved effective in resolving agitation-induced aggregation. The refined bsAb sequences enabled successful mass production in CMC department. The findings of this case study contribute to the understanding of the fundamental mechanism of agitation-induced aggregation and offer a potential protein engineering procedure for addressing similar issues in bsAb. Furthermore, this case study emphasizes the significance of a close partnership between Discovery and CMC teams. Integrating CMC's rigorous evaluation methods with Discovery's engineering capability can facilitate a streamlined development process for bsAb molecules.

Comparative Assessment of Drug Lag for Approved Oncology Targeted Therapies Between Saudi Arabia, the United States, and the European Union

INTRODUCTION

Pharmaceutical regulation on a global scale is a complex process, with regulatory bodies overseeing various aspects, including licensing, registration, manufacturing, marketing, and labeling. Among these, the USFDA plays a crucial role in upholding public health. The pharmaceutical industry contributes significantly to well-being by developing and distributing therapeutic agents. The journey of evaluating new pharmaceuticals involves meticulous examination through several phases, from safety and efficacy assessments to toxicity evaluation. Drug approval involves submitting New Drug Applications (NDAs) to regulatory agencies like the USFDA and EMA. However, disparities in durations contribute to the phenomenon known as "drug lag." This lag refers to delays in a pharmaceutical product's availability in one market compared to another. Addressing this issue is crucial, given its impact on patient access to treatments.

METHOD

This study aims to analyze the extent of drug lag, focusing on newly approved oncology targeted therapies in Saudi Arabia, the United States, and the European Union. Data for cancer treatments authorized by the USFDA, EMA, and SFDA from January 1, 1997, to December 31, 2022, were collected from regulatory agency websites. The data sources included authorization letters, prescription information, and evaluation documents. We conducted a comparative assessment of drug lag for approved oncology targeted therapies between Saudi Arabia, the US, and the EU.

RESULT

Our analysis identified 135 newly approved oncology-targeted drugs within the specified timeframe. Of these, 71 received approval in all three regions, while disparities were evident in others. The USFDA consistently had the highest number of approved drugs, with 98.5% of drugs initially approved there. In contrast, Saudi Arabia had the lowest number of approved drugs and a significantly longer median drug lag, indicating substantial delays in drug availability.

CONCLUSION

This study highlights the significance of mitigating drug lag to enhance global healthcare outcomes and patient access to innovative therapies. Further research and collaborative efforts are essential to bridging these disparities and promoting equitable healthcare worldwide.

The Prodrug DHED Delivers 17β-Estradiol into the Retina for Protection of Retinal Ganglion Cells and Preservation of Visual Function in an Animal Model of Glaucoma

We report a three-pronged phenotypic evaluation of the bioprecursor prodrug 10β,17β-dihydroxyestra-1,4-dien-3-one (DHED) that selectively produces 17β-estradiol (E2) in the retina after topical administration and halts glaucomatous neurodegeneration in a male rat model of the disease. Ocular hypertension (OHT) was induced by hyperosmotic saline injection into an episcleral vein of the eye. Animals received daily DHED eye drops for 12 weeks. Deterioration of visual acuity and contrast sensitivity by OHT in these animals were markedly prevented by the DHED-derived E2 with concomitant preservation of retinal ganglion cells and their axons. In addition, we utilized targeted retina proteomics and a previously established panel of proteins as preclinical biomarkers in the context of OHT-induced neurodegeneration as a characteristic process of the disease. The prodrug treatment provided retina-targeted remediation against the glaucomatous dysregulations of these surrogate endpoints without increasing circulating E2 levels. Collectively, the demonstrated significant neuroprotective effect by the DHED-derived E2 in the selected animal model of glaucoma supports the translational potential of our presented ocular neuroprotective approach owing to its inherent therapeutic safety and efficacy.

Native Joint Septic Arthritis

Native joint septic arthritis (NJSA) is a severe and rapidly progressing joint infection, predominantly bacterial but also potentially fungal or viral, characterized by synovial membrane inflammation and joint damage, necessitating urgent and multidisciplinary management to prevent permanent joint damage and systemic sepsis. Common in large joints like knees, hips, shoulders, and elbows, NJSA's incidence is elevated in individuals with conditions like rheumatoid arthritis, diabetes, immunosuppression, joint replacement history, or intravenous drug use. This review provides a comprehensive overview of NJSA, encompassing its diagnosis, treatment, antibiotic therapy duration, and surgical interventions, as well as the comparison between arthroscopic and open debridement approaches. Additionally, it explores the unique challenges of managing NJSA in patients who have undergone graft anterior cruciate ligament (ACL) reconstruction. The epidemiology, risk factors, pathogenesis, microbiology, clinical manifestations, diagnosis, differential diagnosis, antibiotic treatment, surgical intervention, prevention, and prophylaxis of NJSA are discussed, highlighting the need for prompt diagnosis, aggressive treatment, and ongoing research to enhance patient outcomes.

bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups

The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.

Click Chemistry: Reaction Rates and Their Suitability for Biomedical Applications

Click chemistry has become a commonly used synthetic method due to the simplicity, efficiency, and high selectivity of this class of chemical reactions. Since their initial discovery, further click chemistry methods have been identified and added to the toolbox of click chemistry reactions for biomedical applications. However, selecting the most suitable reaction for a specific application is often challenging, as multiple factors must be considered, including selectivity, reactivity, biocompatibility, and stability. Thus, this review provides an overview of the benefits and limitations of well-established click chemistry reactions with a particular focus on the importance of considering reaction rates, an often overlooked criterion with little available guidance. The importance of understanding each click chemistry reaction beyond simply the reaction speed is discussed comprehensively with reference to recent biomedical research which utilized click chemistry. This review aims to provide a practical resource for researchers to guide the selection of click chemistry classes for different biomedical applications.

Detergent Chemistry Modulates the Transgression of Planetary Boundaries including Antimicrobial Resistance and Drug Discovery

Detergent chemistry enables applications in the world today while harming safe operating spaces that humanity needs for survival. Aim of this review is to support a holistic thought process in the design of detergent chemistry. We harness the planetary boundary concept as a framework for literature survey to identify progresses and knowledge gaps in context with detergent chemistry and five planetary boundaries that are currently transgressed, i.e., climate, freshwater, land system, novel entities, biosphere integrity. Our survey unveils the status of three critical challenges to be addressed in the years to come, including (i) the implementation of a holistically, climate-friendly detergent industry; (ii) the alignment of materialistic and social aspects in creating technical solutions by means of sustainable chemistry; (iii) the development of detergents that serve the purpose of applications but do not harm the biosphere in their role as novel entities. Specifically, medically relevant case reports revealed that even the most sophisticated detergent design cannot sufficiently accelerate drug discovery to outperform the antibiotic resistance development that detergents simultaneously promote as novel entities. Safe operating spaces that humanity needs for its survival may be secured by directing future efforts beyond sustainable chemistry, resource efficiency, and net zero emission targets.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Unveiling Novel Drug Targets and Emerging Therapies for Rheumatoid Arthritis: A Comprehensive Review

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease, that causes joint damage, deformities, and decreased functionality. In addition, RA can also impact organs like the skin, lungs, eyes, and blood vessels. This autoimmune condition arises when the immune system erroneously targets the joint synovial membrane, resulting in synovitis, pannus formation, and cartilage damage. RA treatment is often holistic, integrating medication, physical therapy, and lifestyle modifications. Its main objective is to achieve remission or low disease activity by utilizing a "treat-to-target" approach that optimizes drug usage and dose adjustments based on clinical response and disease activity markers. The primary RA treatment uses disease-modifying antirheumatic drugs (DMARDs) that help to interrupt the inflammatory process. When there is an inadequate response, a combination of biologicals and DMARDs is recommended. Biological therapies target inflammatory pathways and have shown promising results in managing RA symptoms. Close monitoring for adverse effects and disease progression is critical to ensure optimal treatment outcomes. A deeper understanding of the pathways and mechanisms will allow new treatment strategies that minimize adverse effects and maintain quality of life. This review discusses the potential targets that can be used for designing and implementing precision medicine in RA treatment, spotlighting the latest breakthroughs in biologics, JAK inhibitors, IL-6 receptor antagonists, TNF blockers, and disease-modifying noncoding RNAs.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Enhancing cell-based therapies with synthetic gene circuits responsive to molecular stimuli

Synthetic biology aims to contribute to the development of next-generation patient-specific cell-based therapies for chronic diseases especially through the construction of sophisticated synthetic gene switches to enhance the safety and spatiotemporal controllability of engineered cells. Indeed, switches that sense and process specific cues, which may be either externally administered triggers or endogenous disease-associated molecules, have emerged as powerful tools for programming and fine-tuning therapeutic outputs. Living engineered cells, often referred to as designer cells, incorporating such switches are delivered to patients either as encapsulated cell implants or by infusion, as in the case of the clinically approved CAR-T cell therapies. Here, we review recent developments in synthetic gene switches responsive to molecular stimuli, spanning regulatory mechanisms acting at the transcriptional, translational, and posttranslational levels. We also discuss current challenges facing clinical translation of cell-based therapies employing these devices.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Benchmarking compound activity prediction for real-world drug discovery applications

Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a Compound Activity benchmark for Real-world Applications (CARA). Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that although current models can make successful predictions for certain proportions of assays, their performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.

Computational Docking as a Tool in Guiding the Drug Design of Rutaecarpine Derivatives as Potential SARS-CoV-2 Inhibitors

COVID-19 continues to spread around the world. This is mainly because new variants of the SARS-CoV-2 virus emerge due to genomic mutations, evade the immune system and result in the effectiveness of current therapeutics being reduced. We previously established a series of detection platforms, comprising computational docking analysis, S-protein-based ELISA, pseudovirus entry, and 3CL protease activity assays, which allow us to screen a large library of phytochemicals from natural products and to determine their potential in blocking the entry of SARS-CoV-2. In this new screen, rutaecarpine (an alkaloid from Evodia rutaecarpa) was identified as exhibiting anti-SARS-CoV-2 activity. Therefore, we conducted multiple rounds of structure-activity-relationship (SAR) studies around this phytochemical and generated several rutaecarpine analogs that were subjected to in vitro evaluations. Among these derivatives, RU-75 and RU-184 displayed remarkable inhibitory activity when tested in the 3CL protease assay, S-protein-based ELISA, and pseudovirus entry assay (for both wild-type and omicron variants), and they attenuated the inflammatory response induced by SARS-CoV-2. Interestingly, RU-75 and RU-184 both appeared to be more potent than rutaecarpine itself, and this suggests that they might be considered as lead candidates for future pharmacological elaboration.

DrugGym: A testbed for the economics of autonomous drug discovery

Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.

Chemical composition, anti-inflammatory and cytotoxic activity of essential oils from two Luvunga species (L. scandens and L. hongiaoensis) from Vietnam

New essential oils (EOs) extracted from different parts of two Luvunga species (L. scandens and L. hongiaoensis) from Vietnam were investigated for their chemical composition, anti-inflammatory and cytotoxic activity. Sixty-nine total compounds were identified in the EOs by GC/MS. The major constituent of the leaf, fruit, and root EOs from L. scandens was β-caryophyllene (71.5%, 63.0%, and 31.5% respectively). The main compounds in L. hongiaoensis EOs were β-elemene (34.3% in leaf oil) and caryophyllene oxide (21.2% in root oil, 19.4% in stem oil). The EO from L. scandens fruits significantly inhibited nitric oxide production on LPS-induced RAW264.7 cells (IC50 = 37.95 ± 2.76 µg/mL). The EOs from L. hongiaoensis roots and L. scandens leaves and fruits exhibited cytotoxic activity against MCF-7, SK-LU-1, and HepG2 (IC50 from 49.74 ± 3.36 to 97.82 ± 8.61 µg/mL). This is the first report on L. hongiaoensis EOs and significantly complements the composition and bioactivity of L. scandens EOs.

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Potential impact of underlying diseases influencing ADME in nonclinical safety assessment

Nonclinical studies involve in vitro, in silico, and in vivo experiments to assess the toxicokinetics, toxicology, and safety pharmacology of drugs according to regulatory requirements by a national or international authority. In this review, we summarize the potential effects of various underlying diseases governing the absorption, distribution, metabolism, and excretion (ADME) of drugs to consider the use of animal models of diseases in nonclinical trials. Obesity models showed alterations in hepatic metabolizing enzymes, transporters, and renal pathophysiology, which increase the risk of drug-induced toxicity. Diabetes models displayed changes in hepatic metabolizing enzymes, transporters, and glomerular filtration rates (GFR), leading to variability in drug responses and susceptibility to toxicity. Animal models of advanced age exhibited impairment of drug metabolism and kidney function, thereby reducing the drug-metabolizing capacity and clearance. Along with changes in hepatic metabolic enzymes, animal models of metabolic syndrome-related hypertension showed renal dysfunction, resulting in a reduced GFR and urinary excretion of drugs. Taken together, underlying diseases can induce dysfunction of organs involved in the ADME of drugs, ultimately affecting toxicity. Therefore, the use of animal models of representative underlying diseases in nonclinical toxicity studies can be considered to improve the predictability of drug side effects before clinical trials.

GNN-Based Concentration Prediction With Variable Input Flow Rates for Microfluidic Mixers

Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.

Quantifying Imaging Agent Binding and Dissociation in 3-D Cancer Spheroid Tissue Culture Using Paired-Agent Principles

Binding kinetics play an important role in cancer diagnosis and therapeutics. However, current methods of quantifying binding kinetics fail to consider the three-dimensional environment that drugs and imaging agents experience in biological tissue. In response, a methodology to assay agent binding and dissociation in 3-D tissue culture was developed using paired-agent molecular imaging principles. To test the methodology, the uptakes of ABY-029 (an IRDye 800CW-labeled epidermal growth factor receptor (EGFR)-targeted antibody mimetic) and IRDye-700DX carboxylate in 3-D spheroids were measured in four different human cancer cell lines throughout staining and rinsing. A compartment model (optimized for the application) was then fit to the kinetic curves of both imaging agents to estimate binding and dissociation rate constants of the EGFR-targeted ABY-029 agent. A statistically significant correlation was observed between apparent association rate constant (k3) and the receptor concentration experimentally and in simulations (r = 0.99, p < 0.05). A statistically significant difference was found between effective k3 (apparent rate constant of ABY-029 binding to EGFR) values for cell lines with varying levels of EGFR expression (p < 0.05), with no significant difference found between cell lines and controls for other fit parameters. Additionally, a similar binding affinity profile compared to a gold standard method was determined by this model. This low-cost methodology to quantify imaging agent or drug binding affinity in clinically relevant 3-D tumor spheroid models can be used to guide timing of imaging in molecular guided surgery and could have implications in drug development.

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

Rationally Minimizing Natural Product Libraries Using Mass Spectrometry

Natural product libraries are crucial to drug development, but large libraries drastically increase the time and cost during initial high throughput screens. Here, we developed a method that leverages liquid chromatography-tandem mass spectrometry spectral similarity to dramatically reduce library size, with minimal bioactive loss. This method offers a broadly applicable strategy for accelerated drug discovery with cost reductions, which enable implementation in resource-limited settings.

Image-based molecular representation learning for drug development: a survey

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Drug repurposing for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.

Advancements and challenges in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) continues to pose a significant challenge due to the disease complexity and heterogeneous manifestations. Despite recent drug approvals, there remains a critical need for the development of more effective therapies. This review explores the underlying mechanisms involved; including neuroinflammation, glutamate mediated excitotoxicity, mitochondrial dysfunction, and hypermetabolism, and how researchers are trying to develop novel drugs to target these pathways. While progress has been made, the unmet need of ALS patients highlights the urgency for continued research and resource allocation in the pursuit of effective treatments.

Path toward "Net Zero Organic Synthesis"

Researchers over the past ∼200 years have accomplished the synthesis of simple to very complex molecules; however, the concept of ideal synthesis has still not reached maturity. Of late, the "Net Zero" concept has captured the imagination of many fields of technology, in tune with Ideal Synthesis. The current Viewpoint covers the principles of ideal synthesis being discussed in the literature and how one could take up the synthesis of organic molecules considering the Net Zero concept to make this central science well-accepted by critics of this important field.