Exploring different approaches to improve the success of drug discovery and development projects: a review

Enhanced drug classification using machine learning with multiplexed cardiac contractility assays

Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90 % of lead compounds fail to meet safety and efficacy benchmarks during human clinical trials. A preclinical model more representative of the human cardiac response is needed; heart tissue engineered from human pluripotent stem cell derived cardiomyocytes offers such a platform. In this study, three functionally distinct and independently validated engineered cardiac tissue assays are exposed to increasing concentrations of known compounds representing 5 classes of mechanistic action, creating a robust electrophysiology and contractility dataset. Combining results from six individual models, the resulting ensemble algorithm can classify the mechanistic action of unknown compounds with 86.2 % predictive accuracy. This outperforms single-assay models and offers a strategy to enhance future clinical trial success aligned with the recent FDA Modernization Act 2.0.

De novo GTP synthesis is a metabolic vulnerability for the interception of brain metastases

Patients with brain metastases (BM) face a 90% mortality rate within one year of diagnosis and the current standard of care is palliative. Targeting BM-initiating cells (BMICs) is a feasible strategy to treat BM, but druggable targets are limited. Here, we apply Connectivity Map analysis to lung-, breast-, and melanoma-pre-metastatic BMIC gene expression signatures and identify inosine monophosphate dehydrogenase (IMPDH), the rate-limiting enzyme in the de novo GTP synthesis pathway, as a target for BM. We show that pharmacological and genetic perturbation of IMPDH attenuates BMIC proliferation in vitro and the formation of BM in vivo. Metabolomic analyses and CRISPR knockout studies confirm that de novo GTP synthesis is a potent metabolic vulnerability in BM. Overall, our work employs a phenotype-guided therapeutic strategy to uncover IMPDH as a relevant target for attenuating BM outgrowth, which may provide an alternative treatment strategy for patients who are otherwise limited to palliation.

AutoDesigner - Core Design, a De Novo Design Algorithm for Chemical Scaffolds: Application to the Design and Synthesis of Novel Selective Wee1 Inhibitors

The hit identification stage of a drug discovery program generally involves the design of novel chemical scaffolds with desired biological activity against the target(s) of interest. One common approach is scaffold hopping, which is the manual design of novel scaffolds based on known chemical matter. One major limitation of this approach is narrow chemical space exploration, which can lead to difficulties in maintaining or improving biological activity, selectivity, and favorable property space. Another limitation is the lack of preliminary structure-activity relationship (SAR) data around these designs, which could lead to selecting suboptimal scaffolds to advance lead optimization. To address these limitations, we propose AutoDesigner - Core Design (CoreDesign), a de novo scaffold design algorithm. Our approach is a cloud-integrated, de novo design algorithm for systematically exploring and refining chemical scaffolds against biological targets of interest. The algorithm designs, evaluates, and optimizes a vast range, from millions to billions, of molecules in silico, following defined project parameters encompassing structural novelty, physicochemical attributes, potency, and selectivity using active-learning FEP. To validate CoreDesign in a real-world drug discovery setting, we applied it to the design of novel, potent Wee1 inhibitors with improved selectivity over PLK1. Starting from a single known ligand and receptor structure, CoreDesign rapidly explored over 23 billion molecules to identify 1,342 novel chemical series with a mean of 4 compounds per scaffold. To rapidly analyze this large amount of data and prioritize chemical scaffolds for synthesis, we utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) plots of in silico properties. The chemical space projections allowed us to rapidly identify a structurally novel 5-5 fused core meeting all the hit-identification requirements. Several compounds were synthesized and assayed from the scaffold, displaying good potency against Wee1 and excellent PLK1 selectivity. Our results suggest that CoreDesign can significantly speed up the hit-identification process and increase the probability of success of drug discovery campaigns by allowing teams to bring forward high-quality chemical scaffolds derisked by the availability of preliminary SAR.

Antibiotics: From Mechanism of Action to Resistance and Beyond

Antibiotics are the super drugs that have revolutionized modern medicine by curing many infectious diseases caused by various microbes. They efficiently inhibit the growth and multiplication of the pathogenic microbes without causing adverse effects on the host. However, prescribing suboptimal antibiotic and overuse in agriculture and animal husbandry have led to the emergence of antimicrobial resistance, one of the most serious threats to global health at present. The efficacy of a new antibiotic is high when introduced; however, a small bacterial population attains resistance gradually and eventually survives. Understanding the mode of action of these miracle drugs, as well as their interaction with targets is very complex. However, it is necessary to fulfill the constant need for novel therapeutic alternatives to address the inevitable development of resistance. Therefore, considering the need of the hour, this article has been prepared to discuss the mode of action and recent advancements in the field of antibiotics. Efforts has also been made to highlight the current scenario of antimicrobial resistance and drug repurposing as a fast-track solution to combat the issue.

Synthesis, crystal structure, Hirshfeld surface, computational and biological studies of spiro-oxindole derivatives as MDM2-p53 inhibitors

The spiro-oxindole derivatives were synthesized via a 1,3-dipolar cycloaddition approach and characterized by FT-IR, 1H, 13C NMR and mass spectral techniques. The single crystal XRD of 6d further validates the formation of compounds. DFT calculations indicated the reactive nature of compound 6d. Docking studies with 5LAW disclosed the minimum binding energy of - 10.83 kcal/mol for 6d. Furthermore, safe oral bioavailability was ensured by the physicochemical, pharmacokinetic, and toxicity predictions. The anticancer analysis of synthesized compounds showed substantial activity against A549 cells, notably with an IC50 value of 8.13 ± 0.66 µM for 6d compared to standard doxorubicin. 6d was also evaluated for cytotoxicity against L929 healthy cells and A549, showing selectivity towards A549 than healthy cells. AO/EB staining method showed early apoptotic cellular death in the A549 cell line in a dose-dependent manner.

Initial Characterization of the Viridisins' Biological Properties

Viridisin A1 and A2 were previously heterologously expressed, purified, and characterized as ribosomally produced and post-translationally modified lanthipeptides. Such lanthipeptide operons are surprisingly common in Gram-negative bacteria, although their expression seems to be predominantly cryptic under laboratory conditions. However, the bioactivity and biological role of most lanthipeptide operons originating from marine-associated Pseudomonadota, such asThalassomonas viridans XOM25T, have not been described. Therefore, marine-associated Gram-negative lanthipeptide operons represent an untapped resource for novel structures, biochemistries, and bioactivities. Here, the upscaled production of viridisin A1 and A2 was performed for (methyl)lanthionine stereochemistry characterization, antibacterial, antifungal, and larval zebrafish behavioral screening. While antimicrobial activity was not observed, the VirBC modification machinery was found to install both dl- and ll-lanthionine stereoisomers. The VdsA1 and VdsA2 peptides induced sedative and stimulatory effects in zebrafish larvae, respectively, which is a bioactivity not previously reported from lanthipeptides. When combined, VdsA1 and VdsA2 counteracted the sedative and stimulatory effects observed when used individually.

A Perspective on Interdicting in Protein Misfolding for Therapeutic Drug Design: Modulating the Formation of Nonlocal Contacts in α-Synuclein as a Strategy against Parkinson's Disease

In recent work we proposed that interdiction in the earliest contact-formation events along the folding pathway of key viral proteins could provide a novel avenue for therapeutic drug design. In this Perspective we explore the potential applicability of the protein folding interdiction strategy in the realm of neurodegenerative diseases with a specific focus on synucleinopathies. In order to fulfill this goal we review the interdiction proposal and its practical challenges, and we present new results concerning design strategies for possible peptide drugs that could be useful in preventing α-synuclein aggregation.

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.

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.

Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions

Schistosomiasis is a neglected tropical disease which imposes a considerable and enduring impact on affected regions, leading to persistent morbidity, hindering child development, diminishing productivity, and imposing economic burdens. Due to the emergence of drug resistance and limited management options, there is need to develop additional effective inhibitors for schistosomiasis. In view of this, quantitative structure-activity relationship studies, molecular docking, molecular dynamics simulations, drug-likeness and pharmacokinetics predictions were applied to 39 Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR) inhibitors. The chosen QSAR model demonstrated robust statistical parameters, including an R2 of 0.798, R2adj of 0.767, Q2cv of 0.681, LOF of 0.930, R2test of 0.776, and cR2p of 0.746, confirming its reliability. The most active derivative (compound 40) was identified as a lead candidate for the development of new potential non-covalent inhibitors through ligand-based design. Subsequently, 12 novel compounds (40a-40l) were designed with enhanced anti-schistosomiasis activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules. Furthermore, drug-likeness and pharmacokinetics prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for the treatment of schistosomiasis.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

SETDB1 as a cancer target: challenges and perspectives in drug design

Genome stability is governed by chromatin structural dynamics, which modify DNA accessibility under the influence of intra- and inter-nucleosomal contacts, histone post-translational modifications (PTMs) and variations, besides the activity of ATP-dependent chromatin remodelers. These are the main ways by which chromatin dynamics are regulated and connected to nuclear processes, which when dysregulated can frequently be associated with most malignancies. Recently, functional crosstalk between histone modifications and chromatin remodeling has emerged as a critical regulatory method of transcriptional regulation during cell destiny choice. Therefore, improving therapeutic outcomes for patients by focusing on epigenetic targets dysregulated in malignancies should help prevent cancer cells from developing resistance to anticancer treatments. For this reason, SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) has gained a lot of attention recently as a cancer target. SETDB1 is a histone lysine methyltransferase that plays an important role in marking euchromatic and heterochromatic regions. Hence, it promotes the silencing of tumor suppressor genes and contributes to carcinogenesis. Some studies revealed that SETDB1 was overexpressed in various human cancer types, which enhanced tumor growth and metastasis. Thus, SETDB1 appears to be an attractive epigenetic target for new cancer treatments. In this review, we have discussed the effects of its overexpression on the progression of tumors and the development of inhibitor drugs that specifically target this enzyme.

A critical assessment of bioactive compounds databases

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

A Review of Recent Developments in Biopolymer Nano-Based Drug Delivery Systems with Antioxidative Properties: Insights into the Last Five Years

In recent years, biopolymer-based nano-drug delivery systems with antioxidative properties have gained significant attention in the field of pharmaceutical research. These systems offer promising strategies for targeted and controlled drug delivery while also providing antioxidant effects that can mitigate oxidative stress-related diseases. Generally, the healthcare landscape is constantly evolving, necessitating the continual development of innovative therapeutic approaches and drug delivery systems (DDSs). DDSs play a pivotal role in enhancing treatment efficacy, minimizing adverse effects, and optimizing patient compliance. Among these, nanotechnology-driven delivery approaches have garnered significant attention due to their unique properties, such as improved solubility, controlled release, and targeted delivery. Nanomaterials, including nanoparticles, nanocapsules, nanotubes, etc., offer versatile platforms for drug delivery and tissue engineering applications. Additionally, biopolymer-based DDSs hold immense promise, leveraging natural or synthetic biopolymers to encapsulate drugs and enable targeted and controlled release. These systems offer numerous advantages, including biocompatibility, biodegradability, and low immunogenicity. The utilization of polysaccharides, polynucleotides, proteins, and polyesters as biopolymer matrices further enhances the versatility and applicability of DDSs. Moreover, substances with antioxidative properties have emerged as key players in combating oxidative stress-related diseases, offering protection against cellular damage and chronic illnesses. The development of biopolymer-based nanoformulations with antioxidative properties represents a burgeoning research area, with a substantial increase in publications in recent years. This review provides a comprehensive overview of the recent developments within this area over the past five years. It discusses various biopolymer materials, fabrication techniques, stabilizers, factors influencing degradation, and drug release. Additionally, it highlights emerging trends, challenges, and prospects in this rapidly evolving field.

Exploring regulatory flexibility to create novel incentives to optimize drug discovery

Efforts by governments, firms, and patients to deliver pioneering drugs for critical health needs face a challenge of diminishing efficiency in developing those medicines. While multi-sectoral collaborations involving firms, researchers, patients, and policymakers are widely recognized as crucial for countering this decline, existing incentives to engage in drug development predominantly target drug manufacturers and thereby do little to stimulate collaborative innovation. In this mini review, we consider the unexplored potential within pharmaceutical regulations to create novel incentives to encourage a diverse set of actors from the public and private spheres to engage in the kind of collaborative knowledge exchange requisite for fostering enhanced innovation in early drug development.

Alrobaian M. Strengths and weaknesses of current and future prospects of artificial intelligence-mounted technologies applied in the development of pharmaceutical products and services

Starting from drug discovery, through research and development, to clinical trials and FDA approval, artificial intelligence (AI) plays a vital role in planning, developing, assessing modelling, and optimization of product attributes. In recent decades, machine-learning algorithms integrated into artificial neural networks, neuro-fuzzy logic and decision trees have been applied to tremendous domains related to drug formulation development. Optimized formulations were transformed from lab to market based on optimized properties derived from AI Technologies. Research and development in pharmaceutical industry rely upon computer-driven equipment and machine learning technology to extract data, perform simulations, modelling, and optimization to get optimum solutions. Merging AI technologies in various steps of pharmaceutical manufacture is a major challenge due to lack of in-house technologies. In silico studies based on artificial intelligence are widely applied as effective tools to screen the market needs of medications and pharmaceutical services through inspecting scientific literature and prioritizing medicines for specific illnesses or a particular patient. Specialized personnel who excel in scientific and data science with analytical knowledge are essential for transformation to smart manufacturing and offering services. However, privacy, cybersecurity, AI-dependent unemployment, and ownership rights of AI technologies require proper regulations to gain the benefits and minimize the drawbacks.

Degraders upgraded: the rise of PROTACs in hematological malignancies

ABSTRACT

Targeted protein degradation (TPD) is a revolutionary approach to targeted therapy in hematological malignancies that potentially circumvents many constraints of existing small-molecule inhibitors. Heterobifunctional proteolysis-targeting chimeras (PROTACs) are the leading TPD drug class, with numerous agents now in clinical trials for a range of blood cancers. PROTACs harness the cell-intrinsic protein recycling infrastructure, the ubiquitin-proteasome system, to completely degrade target proteins. Distinct from targeted small-molecule inhibitor therapies, PROTACs can eliminate critical but conventionally "undruggable" targets, overcome resistance mechanisms to small-molecule therapies, and can improve tissue specificity and off-target toxicity. Orally bioavailable, PROTACs are not dependent on the occupancy-driven pharmacology inherent to inhibitory therapeutics, facilitating substoichiometric dosing that does not require an active or allosteric target binding site. Preliminary clinical data demonstrate promising therapeutic activity in heavily pretreated populations and novel technology platforms are poised to exploit a myriad of permutations of PROTAC molecular design to enhance efficacy and targeting specificity. As the field rapidly progresses and various non-PROTAC TPD drug candidates emerge, this review explores the scientific and preclinical foundations of PROTACs and presents them within common clinical contexts. Additionally, we examine the latest findings from ongoing active PROTAC clinical trials.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

The use of artificial intelligence in the treatment of rare diseases: A scoping review

With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.

Geometric Deep Learning for Structure-Based Ligand Design

A pervasive challenge in drug design is determining how to expand a ligand-a small molecule that binds to a target biomolecule-in order to improve various properties of the ligand. Adding single chemical groups, known as fragments, is important for lead optimization tasks, and adding multiple fragments is critical for fragment-based drug design. We have developed a comprehensive framework that uses machine learning and three-dimensional protein-ligand structures to address this challenge. Our method, FRAME, iteratively determines where on a ligand to add fragments, selects fragments to add, and predicts the geometry of the added fragments. On a comprehensive benchmark, FRAME consistently improves predicted affinity and selectivity relative to the initial ligand, while generating molecules with more drug-like chemical properties than docking-based methods currently in widespread use. FRAME learns to accurately describe molecular interactions despite being given no prior information on such interactions. The resulting framework for quality molecular hypothesis generation can be easily incorporated into the workflows of medicinal chemists for diverse tasks, including lead optimization, fragment-based drug discovery, and de novo drug design.

Investigating the Cell Entry Mechanism, Disassembly, and Toxicity of the Nanocage PCC-1: Insights into Its Potential as a Drug Delivery Vehicle

The porous coordination cage PCC-1 represents a new platform potentially useful for the cellular delivery of drugs with poor cell permeability and solubility. PCC-1 is a metal-organic polyhedron constructed from zinc metal ions and organic ligands through coordination bonds. PCC-1 possesses an internal cavity that is suitable for drug encapsulation. To better understand the biocompatibility of PCC-1 with human cells, the cell entry mechanism, disassembly, and toxicity of the nanocage were investigated. PCC-1 localizes in the nuclei and cytoplasm within minutes upon incubation with cells, independent of endocytosis and cargo, suggesting direct plasma membrane translocation of the nanocage carrying its guest in its internal cavity. Furthermore, the rates of cell entry correlate to extracellular concentrations, indicating that PCC-1 is likely diffusing passively through the membrane despite its relatively large size. Once inside cells, PCC-1 disintegrates into zinc metal ions and ligands over a period of several hours, each component being cleared from cells within 1 day. PCC-1 is relatively safe for cells at low micromolar concentrations but becomes inhibitory to cell proliferation and toxic above a concentration or incubation time threshold. However, cells surviving these conditions can return to homeostasis 3-5 days after exposure. Overall, these findings demonstrate that PCC-1 enters live cells by crossing biological membranes spontaneously. This should prove useful to deliver drugs that lack this capacity on their own, provided that the dosage and exposure time are controlled to avoid toxicity.

Forward or Backward: Lessons Learned from Small Molecule Drugs Approved by FDA from 2012 to 2022

At every juncture in history, the design and identification of new drugs pose significant challenges. To gain valuable insights for future drug development, we conducted a detailed analysis of New Molecular Entitiy (NME) approved by the Food and Drug Administration (FDA) from 2012 to 2022 and focused on the analysis of first-in-class (FIC) small-molecules from a perspective of a medicinal chemist. We compared the change of numbers between all the FDA-approved NMEs and FIC, which could be more visual to analyze the changing trend of FIC. To get a more visual change of molecular physical properties, we computed the annual average trends in molecular weight for FIC across various therapeutic fields. Furthermore, we consolidated essential information into three comprehensive databases, which covered the indications, canonical SMILES, structural formula, research and development (R&D) institutions, molecular weight, calculated LogP (CLogP), and route of administration on all the small-molecule pharmaceutical. Through the analysis of the database of 11 years of approvals, we forecast the development trend of NME approval in the future.

Experimental and Theoretical Analysis of Tricyclic Antidepressants by Ultraviolet Picosecond Laser Desorption Post-Ionization Mass Spectrometry

Imipramine class tricyclic antidepressants have low ionization efficiencies that make them difficult to detect by using secondary ion mass spectrometry. Ultraviolet picosecond laser desorption postionization (ps-LDPI-MS) is examined here for the detection of four tricyclic antidepressants: imipramine, desipramine, amitriptyline, and clomipramine. About 30 ps laser pulses at either 213 nm (5.8 eV) or 355 nm (3.5 eV) are used for desorption of samples under vacuum, 7.9 eV (157 nm) fluorine laser pulses are used for post-ionization, and the ions so formed are detected by time-of-flight mass spectrometry. Detection of imipramine by 213 nm ps-LDPI-MS shows less fragmentation than either 355 nm ps-LDPI-MS or prior results from 800 nm fs-LDPI-MS. Ionization energies of imipramine, desipramine, amitriptyline, and clomipramine are predicted using density functional theory calculations and used to explain the corresponding ps-LDPI-MS data for these four compounds as resulting from single-photon ionization. The experimental observation of low-mass amine-containing fragments with calculated ionization energies below 7.9 eV is attributed mostly to dissociation during laser desorption, followed by single-photon ionization of the neutral fragments rather than the more traditional mechanism of unimolecular dissociation following single-photon ionization of the parent molecule.

Screening Drugs for Broad-Spectrum, Host-Directed Antiviral Activity: Lessons from the Development of Probenecid for COVID-19

In the early stages of drug discovery, researchers develop assays that are compatible with high throughput screening (HTS) and structure activity relationship (SAR) measurements. These assays are designed to evaluate the effectiveness of new and known molecular entities, typically targeting specific features within the virus. Drugs that inhibit virus replication by inhibiting a host gene or pathway are often missed because the goal is to identify active antiviral agents against known viral targets. Screening efforts should be sufficiently robust to identify all potential targets regardless of the antiviral mechanism to avoid misleading conclusions.

The role of methionine synthases in fungal metabolism and virulence

Methionine synthases (MetH) catalyse the methylation of homocysteine (Hcy) with 5-methyl-tetrahydrofolate (5, methyl-THF) acting as methyl donor, to form methionine (Met) and tetrahydrofolate (THF). This function is performed by two unrelated classes of enzymes that differ significantly in both their structures and mechanisms of action. The genomes of plants and many fungi exclusively encode cobalamin-independent enzymes (EC.2.1.1.14), while some fungi also possess proteins from the cobalamin-dependent (EC.2.1.1.13) family utilised by humans. Methionine synthase's function connects the methionine and folate cycles, making it a crucial node in primary metabolism, with impacts on important cellular processes such as anabolism, growth and synthesis of proteins, polyamines, nucleotides and lipids. As a result, MetHs are vital for the viability or virulence of numerous prominent human and plant pathogenic fungi and have been proposed as promising broad-spectrum antifungal drug targets. This review provides a summary of the relevance of methionine synthases to fungal metabolism, their potential as antifungal drug targets and insights into the structures of both classes of MetH.

Bridging the gap between tumor-on-chip and clinics: a systematic review of 15 years of studies

Over the past 15 years, the field of oncology research has witnessed significant progress in the development of new cell culture models, such as tumor-on-chip (ToC) systems. In this comprehensive overview, we present a multidisciplinary perspective by bringing together physicists, biologists, clinicians, and experts from pharmaceutical companies to highlight the current state of ToC research, its unique features, and the challenges it faces. To offer readers a clear and quantitative understanding of the ToC field, we conducted an extensive systematic analysis of more than 300 publications related to ToC from 2005 to 2022. ToC offer key advantages over other in vitro models by enabling precise control over various parameters. These parameters include the properties of the extracellular matrix, mechanical forces exerted on cells, the physico-chemical environment, cell composition, and the architecture of the tumor microenvironment. Such fine control allows ToC to closely replicate the complex microenvironment and interactions within tumors, facilitating the study of cancer progression and therapeutic responses in a highly representative manner. Importantly, by incorporating patient-derived cells or tumor xenografts, ToC models have demonstrated promising results in terms of clinical validation. We also examined the potential of ToC for pharmaceutical industries in which ToC adoption is expected to occur gradually. Looking ahead, given the high failure rate of clinical trials and the increasing emphasis on the 3Rs principles (replacement, reduction, refinement of animal experimentation), ToC models hold immense potential for cancer research. In the next decade, data generated from ToC models could potentially be employed for discovering new therapeutic targets, contributing to regulatory purposes, refining preclinical drug testing and reducing reliance on animal models.

An electroaffinity labelling platform for chemoproteomic-based target identification

Target identification involves deconvoluting the protein target of a pharmacologically active, small-molecule ligand, a process that is critical for early drug discovery yet technically challenging. Photoaffinity labelling strategies have become the benchmark for small-molecule target deconvolution, but covalent protein capture requires the use of high-energy ultraviolet light, which can complicate downstream target identification. Thus, there is a strong demand for alternative technologies that allow for controlled activation of chemical probes to covalently label their protein target. Here we introduce an electroaffinity labelling platform that leverages the use of a small, redox-active diazetidinone functional group to enable chemoproteomic-based target identification of pharmacophores within live cell environments. The underlying discovery to enable this platform is that the diazetidinone can be electrochemically oxidized to reveal a reactive intermediate useful for covalent modification of proteins. This work demonstrates the electrochemical platform to be a functional tool for drug-target identification.

Design of Tracers in Fluorescence Polarization Assay for Extensive Application in Small Molecule Drug Discovery

Development of fluorescence polarization (FP) assays, especially in a competitive manner, is a potent and mature tool for measuring the binding affinities of small molecules. This approach is suitable for high-throughput screening (HTS) for initial ligands and is also applicable for further study of the structure-activity relationships (SARs) of candidate compounds for drug discovery. Buffer and tracer, especially rational design of the tracer, play a vital role in an FP assay system. In this perspective, we provided different kinds of approaches for tracer design based on successful cases in recent years. We classified these tracers by different types of ligands in tracers, including peptide, nucleic acid, natural product, and small molecule. To make this technology accessible for more targets, we briefly described the basic theory and workflow, followed by highlighting the design and application of typical FP tracers from a perspective of medicinal chemistry.

Evaluation of the Usefulness of Topological Indices for Predicting Selected Physicochemical Properties of Bioactive Substances with Anti-Androgenic and Hypouricemic Activity

Due to the observed increase in the importance of computational methods in determining selected physicochemical parameters of biologically active compounds that are key to understanding their ADME/T profile, such as lipophilicity, there is a great need to work on accurate and precise in silico models based on some structural descriptors, such as topological indices for predicting lipophilicity of certain anti-androgenic and hypouricemic agents and their derivatives, for which the experimental lipophilicity parameter is not accurately described in the available literature, e.g., febuxostat, oxypurinol, ailanthone, abiraterone and teriflunomide. Therefore, the following topological indices were accurately calculated in this paper: Gutman (M, Mν), Randić (0χ, 1χ, 0χν, 1χν), Wiener (W), Rouvray-Crafford (R) and Pyka (A, 0B, 1B) for the selected anti-androgenic drugs (abiraterone, bicalutamide, flutamide, nilutamide, leflunomide, teriflunomide, ailanthone) and some hypouricemic compounds (allopurinol, oxypurinol, febuxostat). Linear regression analysis was used to create simple linear correlations between the newly calculated topological indices and some physicochemical parameters, including lipophilicity descriptors of the tested compounds (previously obtained by TLC and theoretical methods). Our studies confirmed the usefulness of the obtained linear regression equations based on topological indices to predict ADME/T important parameters, such as lipophilicity descriptors of tested compounds with anti-androgenic and hypouricemic effects. The proposed calculation method based on topological indices is fast, easy to use and avoids valuable and lengthy laboratory experiments required in the case of experimental ADME/T studies.

Integrating Early-Stage Drug Development with Clinical Networks; Challenges and Opportunities: The City of Hope Developing Experience

Recent data suggest that patients with advanced cancer who participate in biomarker/genomically informed early-stage clinical trials experience clinical benefit. While most early-stage clinical trials are conducted in major academic centers, the majority of cancer patients in the United States are treated in community practices. Here, we describe ongoing efforts at the City of Hope Cancer Center to integrate our network community oncology clinical practices into our academic, centralized biomarker/genomic-driven, early-stage clinical trial program to build an understanding of the approaches that provide the benefits of early-stage clinical trial participation to community patients. Our efforts include three key initiatives: the development of a virtual "Refractory Disease" phase 1 trial matching televideo clinic, the construction of infrastructure to support the expansion of phase 1 clinical trials to a distant regional clinical satellite hub, and the implementation of an enterprise-wide precision medicine, germline, and somatic testing program. Our work at City of Hope may serve as an example to facilitate similar efforts at other institutions.

Development of Hyphenated Techniques and Network Identification Approaches for Biotransformational Evaluation of Promising Antitubercular N-pyrrolyl hydrazide-hydrazone in Isolated Rat Hepatocytes

Novel, rapid and precise RP-HPLC-DAD method was developed, validated and successfully applied for determination of metabolic changes of ethyl 5-(4-bromophenyl)-1-(3-(2-(2-hydroxybenzylidene)hydrazinyl)-3-oxopropyl)-2-methyl-1H-pyrrole-3-carboxylate (12b) in isolated rat hepatocytes. The analytes were detected by a simple DAD detector at 279 nm wavelength. A single-step extraction method was implemented to enable fast purification and extraction from cellular culture, resulting in a complete recovery. Thereafter, the method was adequately transferred to a LC-MS system for identification of unknown products. Additionally, network metabolism evaluation was performed to predict the structures of major metabolites with their isotope mass through BioTransformer 3.0. The data from the LC-MS analysis and the online server were compared for comprehensive identification. The results indicated formation of four metabolic products, obtained through processes of hydrolysis (12 and b), hydroxylation in the structure 12b (M1) and O-dealkylation (M2).

Current trends in natural products for the treatment and management of dementia: Computational to clinical studies

The number of preclinical and clinical studies evaluating natural products-based management of dementia has gradually increased, with an exponential rise in 2020 and 2021. Keeping this in mind, we examined current trends from 2016 to 2021 in order to assess the growth potential of natural products in the treatment of dementia. Publicly available literature was collected from various databases like PubMed and Google Scholar. Oxidative stress-related targets, NF-κB pathway, anti-tau aggregation, anti-AChE, and A-β aggregation were found to be common targets and pathways. A retrospective analysis of 33 antidementia natural compounds identified 125 sustainable resources distributed among 65 families, 39 orders, and 7 classes. We found that families such as Berberidaceae, Zingiberaceae, and Fabaceae, as well as orders such as Lamiales, Sapindales, and Myrtales, appear to be important and should be researched further for antidementia compounds. Moreover, some natural products, such as quercetin, curcumin, icariside II, berberine, and resveratrol, have a wide range of applications. Clinical studies and patents support the importance of dietary supplements and natural products, which we will also discuss. Finally, we conclude with the broad scope, future challenges, and opportunities for field researchers.

Structure-Based Virtual Screening and Molecular Dynamics Simulation Assessments of Depsidones as Possible Selective Cannabinoid Receptor Type 2 Agonists

The discovery of natural drug metabolites is a leading contributor to fulfilling the sustainable development goal of finding solutions to global health challenges. Depsidones are a class of polyketides that have been separated from lichens, fungi, sponges, and plants and possess various bioactivities, including cytotoxic, antimicrobial, antimalarial, antituberculosis, acetylcholinesterase and α-glucosidase inhibition, and anti-inflammatory effects. Endocannabinoid receptors (CB1 and CB2) are G-protein-coupled receptors (GPCRs), and their activation mediates many physiological processes. CB1 is the dominant subtype in the central nervous system, while CB2 is mainly expressed in the immune system. The two receptors exhibit high heterogeneity, making developing selective ligands a great challenge. Attempts to develop CB2 selective agonists for treating inflammatory diseases and neuropathic pain have not been successful due to the high homology of the binding sites of the CB receptors. In this work, 235 depsidones from various sources were investigated for the possibility of identifying CB2-selective agonists by performing multiple docking studies, including induced fit docking and Prime/molecular mechanics-generalized Born surface area (MM-GBSA) calculations to predict the binding mode and free energy. Simplicildone J (10), lobaric acid (110), mollicellin Q (101), garcinisidone E (215), mollicellin P (100), paucinervin Q (149), and boremexin C (161) had the highest binding scores (-12.134 kcal/mol, -11.944 kcal/mol, -11.479 kcal/mol, -11.394 kcal/mol, -11.322 kcal/mol, -11.305 kcal/mol, and -11.254 kcal/mol, respectively) when screened against the CB2 receptor (PDB ID: 6KPF). The molecular dynamic simulation was performed on the compounds with the highest binding scores. The computational outcomes show that garcinisidone E (215) and paucinervin Q (149) could be substantial candidates for CB2 receptor activation and warrant further in vivo and in vitro investigations.

African animal trypanocide resistance: A systematic review and meta-analysis

Background

African animal trypanocide resistance (AATr) continues to undermine global efforts to eliminate the transmission of African trypanosomiasis in endemic communities. The continued lack of new trypanocides has precipitated drug misuse and overuse, thus contributing to the development of the AATr phenotype. In this study, we investigated the threat associated with AATr by using the major globally available chemotherapeutical agents.

Methods

A total of seven electronic databases were screened for an article on trypanocide resistance in AATr by using keywords on preclinical and clinical trials with the number of animals with treatment relapse, days taken to relapse, and resistant gene markers using the PRISMA checklist. Data were cleaned using the SR deduplicator and covidence and analyzed using Cochrane RevMan®. Dichotomous outputs were presented using risk ratio (RR), while continuous data were presented using the standardized mean difference (SMD) at a 95% confidence interval.

Results

A total of eight publications in which diminazene aceturate (DA), isometamidium chloride (ISM), and homidium chloride/bromide (HB) were identified as the major trypanocides were used. In all preclinical studies, the development of resistance was in the order of HB > ISM > DA. DA vs. ISM (SMD = 0.15, 95% CI: -0.54, 0.83; I ² = 46%, P = 0.05), DA vs. HB (SMD = 0.96, 95% CI: 0.47, 1.45; I ² = 0%, P = 0.86), and HB vs. ISM (SMD = -0.41, 95% CI: -0.96, 0.14; I ² = 5%, P = 0.38) showed multiple cross-resistance. Clinical studies also showed evidence of multi-drug resistance on DA and ISM (RR = 1.01, 95% CI: 0.71-1.43; I ² = 46%, P = 0.16). To address resistance, most preclinical studies increased the dosage and the treatment time, and this failed to improve the patient's prognosis. Major markers of resistance explored include TbAT1, P1/P2 transporters, folate transporters, such as F-I, F-II, F-III, and polyamine biosynthesis inhibitors. In addition, immunosuppressed hosts favor the development of AATr.

Conclusion

AATr is a threat that requires a shift in the current disease control strategies in most developing nations due to inter-species transmission. Multi-drug cross-resistance against the only accessible trypanocides is a major public health risk, justifying the need to revise the policy in developing countries to promote control of African trypanosomiasis.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study

Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC. A set of comprehensive pharmacoinformatics approaches was implemented and, finally, seventeen potential molecules were found to be effective for the modulation of hRRM1 activity. Molecular docking, negative image-based ShaEP scoring, absolute binding free energy, in silico pharmacokinetics, and toxicity assessments corroborated the potentiality of the selected molecules. Almost all molecules showed higher affinity in comparison to gemcitabine and naphthyl salicylic acyl hydrazone (NSAH). On binding interaction analysis, a number of critical amino acids was found to hold the molecules at the active site cavity. The molecular dynamics (MD) simulation study also indicated the stability between protein and ligands. High negative MM-GBSA (molecular mechanics generalized Born and surface area) binding free energy indicated the potentiality of the molecules. Therefore, the proposed molecules might have the potential to be effective therapeutics for the management of BTC.

Evolving Experimental Techniques for Structure-Based Drug Design

Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization.

Cannabinoids and Chronic Liver Diseases

Nonalcoholic fatty liver disease (NAFLD), alcohol-induced liver disease (ALD), and viral hepatitis are the main causes of morbidity and mortality related to chronic liver diseases (CLDs) worldwide. New therapeutic approaches to prevent or reverse these liver disorders are thus emerging. Although their etiologies differ, these CLDs all have in common a significant dysregulation of liver metabolism that is closely linked to the perturbation of the hepatic endocannabinoid system (eCBS) and inflammatory pathways. Therefore, targeting the hepatic eCBS might have promising therapeutic potential to overcome CLDs. Experimental models of CLDs and observational studies in humans suggest that cannabis and its derivatives may exert hepatoprotective effects against CLDs through diverse pathways. However, these promising therapeutic benefits are not yet fully validated, as the few completed clinical trials on phytocannabinoids, which are thought to hold the most promising therapeutic potential (cannabidiol or tetrahydrocannabivarin), remained inconclusive. Therefore, expanding research on less studied phytocannabinoids and their derivatives, with a focus on their mode of action on liver metabolism, might provide promising advances in the development of new and original therapeutics for the management of CLDs, such as NAFLD, ALD, or even hepatitis C-induced liver disorders.

Bench to bedside: The ambitious goal of transducing medicinal chemistry from the lab to the clinic

This paper deals with a critical examination on the possibility of quantitatively predicting the in vivo activity of new chemical entities (NCEs) by making use of in silico and in vitro data including three-dimensional structure of drug-target complex, thermodynamic and crowding parameters, ADME (absorption, distribution, metabolism, excretion) properties, and off-target (toxic) interactions. This formidable challenge is still a dream, given the presently occurring exceedingly high (>95%) attrition rates of NCEs. As a solution we envisage exploiting advanced AI (artificial intelligence) algorithms. In fact, very recent AI implemented programs proved remarkably effective and accurate in predicting the 3D architecture of (any) protein, starting from the amino-acid sequence only. The same accuracy could not be obtained using classical conformational studies. Apart from these breakthrough results, AI algorithms could be profitably used to extract valuable information from the huge amount of data so far accumulated from previous studies. In case of positive results, the drug discovery procedure would be sensibly accelerated, and the relative costs remarkably reduced.

Why 90% of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies, which raised the question whether certain aspects in target validation and drug optimization are overlooked? Current drug optimization overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity in disease/normal tissues using structure‒tissue exposure/selectivity-relationship (STR), which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity. We propose structure‒tissue exposure/selectivity-activity relationship (STAR) to improve drug optimization, which classifies drug candidates based on drug's potency/selectivity, tissue exposure/selectivity, and required dose for balancing clinical efficacy/toxicity. Class I drugs have high specificity/potency and high tissue exposure/selectivity, which needs low dose to achieve superior clinical efficacy/safety with high success rate. Class II drugs have high specificity/potency and low tissue exposure/selectivity, which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated. Class III drugs have relatively low (adequate) specificity/potency but high tissue exposure/selectivity, which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked. Class IV drugs have low specificity/potency and low tissue exposure/selectivity, which achieves inadequate efficacy/safety, and should be terminated early. STAR may improve drug optimization and clinical studies for the success of clinical drug development.

Measuring Protein Aggregation and Stability Using High-Throughput Biophysical Approaches

Structure-function relationships of biological macromolecules, in particular proteins, provide crucial insights for fundamental biochemistry, medical research and early drug discovery. However, production of recombinant proteins, either for structure determination, functional studies, or to be used as biopharmaceutical products, is often hampered by their instability and propensity to aggregate in solution in vitro. Protein samples of poor quality are often associated with reduced reproducibility as well as high research and production expenses. Several biophysical methods are available for measuring protein aggregation and stability. Yet, discovering and developing means to improve protein behaviour and structure-function integrity remains a demanding task. Here, we discuss workflows that are made possible by adapting established biophysical methods to high-throughput screening approaches. Rapid identification and optimisation of conditions that promote protein stability and reduce aggregation will support researchers and industry to maximise sample quality, stability and reproducibility, thereby reducing research and development time and costs.

Current Strategies to Enhance Delivery of Drugs across the Blood–Brain Barrier

The blood–brain barrier (BBB) has shown to be a significant obstacle to brain medication delivery. The BBB in a healthy brain is a diffusion barrier that prevents most substances from passing from the blood to the brain; only tiny molecules can pass across the BBB. The BBB is disturbed in specific pathological illnesses such as stroke, diabetes, seizures, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease. The goal of this study is to offer a general overview of current brain medication delivery techniques and associated topics from the last five years. It is anticipated that this review will stimulate readers to look into new ways to deliver medications to the brain. Following an introduction of the construction and function of the BBB in both healthy and pathological conditions, this review revisits certain contested questions, such as whether nanoparticles may cross the BBB on their own and if medications are selectively delivered to the brain by deliberately targeted nanoparticles. Current non-nanoparticle options are also discussed, including drug delivery via the permeable BBB under pathological circumstances and the use of non-invasive approaches to improve brain medication absorption.

Experiences From Developing Software for Large X-Ray Crystallography-Driven Protein-Ligand Studies

The throughput of macromolecular X-ray crystallography experiments has surged over the last decade. This remarkable gain in efficiency has been facilitated by increases in the availability of high-intensity X-ray beams, (ultra)fast detectors and high degrees of automation. These developments have in turn spurred the development of several dedicated centers for crystal-based fragment screening which enable the preparation and collection of hundreds of single-crystal diffraction datasets per day. Crystal structures of target proteins in complex with small-molecule ligands are of immense importance for structure-based drug design (SBDD) and their rapid turnover is a prerequisite for accelerated development cycles. While the experimental part of the process is well defined and has by now been established at several synchrotron sites, it is noticeable that software and algorithmic aspects have received far less attention, as well as the implications of new methodologies on established paradigms for structure determination, analysis, and visualization. We will review three key areas of development of large-scale protein-ligand studies. First, we will look into new software developments for batch data processing, followed by a discussion of the methodological changes in the analysis, modeling, refinement and deposition of structures for SBDD, and the changes in mindset that these new methods require, both on the side of depositors and users of macromolecular models. Finally, we will highlight key new developments for the presentation and analysis of the collections of structures that these experiments produce, and provide an outlook for future developments.

Protein Folding Interdiction Strategy for Therapeutic Drug Development in Viral Diseases: Ebola VP40 and Influenza A M1

In a recent paper, we proposed the folding interdiction target region (FITR) strategy for therapeutic drug design in SARS-CoV-2. This paper expands the application of the FITR strategy by proposing therapeutic drug design approaches against Ebola virus disease and influenza A. We predict target regions for folding interdicting drugs on correspondingly relevant structural proteins of both pathogenic viruses: VP40 of Ebola, and matrix protein M1 of influenza A. Identification of the protein targets employs the sequential collapse model (SCM) for protein folding. It is explained that the model predicts natural peptide candidates in each case from which to start the search for therapeutic drugs. The paper also discusses how these predictions could be tested, as well as some challenges likely to be found when designing effective therapeutic drugs from the proposed peptide candidates. The FITR strategy opens a potential new avenue for the design of therapeutic drugs that promises to be effective against infectious diseases.