Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

Long way up: rethink diseases in light of phase separation and phase transition

Biomolecular condensation, driven by multivalency, serves as a fundamental mechanism within cells, facilitating the formation of distinct compartments, including membraneless organelles that play essential roles in various cellular processes. Perturbations in the delicate equilibrium of condensation, whether resulting in gain or loss of phase separation, have robustly been associated with cellular dysfunction and physiological disorders. As ongoing research endeavors wholeheartedly embrace this newly acknowledged principle, a transformative shift is occurring in our comprehension of disease. Consequently, significant strides have been made in unraveling the profound relevance and potential causal connections between abnormal phase separation and various diseases. This comprehensive review presents compelling recent evidence that highlight the intricate associations between aberrant phase separation and neurodegenerative diseases, cancers, and infectious diseases. Additionally, we provide a succinct summary of current efforts and propose innovative solutions for the development of potential therapeutics to combat the pathological consequences attributed to aberrant phase separation.

Advancements in nanomedicine: Precision delivery strategies for male pelvic malignancies - Spotlight on prostate and colorectal cancer

BACKGROUND

Pelvic malignancies consistently pose significant global health challenges, adversely affecting the well-being of the male population. It is anticipated that clinicians will continue to confront these cancers in their practice. Nanomedicine offers promising strategies that revolutionize the treatment of male pelvic malignancies by providing precise delivery methods that aim to improve the efficacy of therapeutic outcomes while minimizing side effects. Nanoparticles are designed to encapsulate therapeutic agents and selectively target cancer cells. They can also be loaded with theragnostic agents, enabling multifunctional capabilities.

OBJECTIVE

This review aims to summarize the latest nanomedicine research into clinical applications, focusing on nanotechnology-based treatment strategies for male pelvic malignancies, encompassing chemotherapy, radiotherapy, immunotherapy, and other cutting-edge therapies. The review is structured to assist physicians, particularly those with limited knowledge of biochemistry and bioengineering, in comprehending the functionalities and applications of nanomaterials.

METHODS

Multiple databases, including PubMed, the National Library of Medicine, and Embase, were utilized to locate and review recently published articles on advancements in nano-drug delivery for prostate and colorectal cancers.

CONCLUSION

Nanomedicine possesses considerable potential in improving therapeutic outcomes and reducing adverse effects for male pelvic malignancies. Through precision delivery methods, this emerging field presents innovative treatment modalities to address these challenging diseases. Nevertheless, the majority of current studies are in the preclinical phase, with a lack of sufficient evidence to fully understand the precise mechanisms of action, absence of comprehensive pharmacotoxicity profiles, and uncertainty surrounding long-term consequences.

When Protein Structure Embedding Meets Large Language Models

Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machine learning algorithms to categorize structures based on data from databases like the Protein Data Bank (PDB). To predict protein functions, embeddings based on protein sequences have been employed. Creating numerical embeddings that preserve vital information while considering protein structure and sequence presents several challenges. The existing literature lacks a comprehensive and effective approach that combines structural and sequence-based features to achieve efficient protein classification. While large language models (LLMs) have exhibited promising outcomes for protein function prediction, their focus primarily lies on protein sequences, disregarding the 3D structures of proteins. The quality of embeddings heavily relies on how well the geometry of the embedding space aligns with the underlying data structure, posing a critical research question. Traditionally, Euclidean space has served as a widely utilized framework for embeddings. In this study, we propose a novel method for designing numerical embeddings in Euclidean space for proteins by leveraging 3D structure information, specifically employing the concept of contact maps. These embeddings are synergistically combined with features extracted from LLMs and traditional feature engineering techniques to enhance the performance of embeddings in supervised protein analysis. Experimental results on benchmark datasets, including PDB Bind and STCRDAB, demonstrate the superior performance of the proposed method for protein function prediction.

Docking Studies on Biomolecules from Marine Microalga Skeletonema costatum Against Hemolysin Protein of Bioluminescence Disease-Causing Vibrio harveyi

Grow-out and hatchery units of shrimps are being impacted by disease-causing bacterial pathogens and predominantly marine Vibrios. The use of chemicals for governing bacterial pathogens in the aquaculture practices is developing resistance to bacteria. Henceforth, the application of bio-therapeutic agents from marine resources for controlling pathogens is most vital to be considered. Molecular docking is computer-assisted drug design tool to detect and counteract for drug-receptor interaction for known target protein of diseases. Therefore, an effort was made with the extract of the marine micro alga Skeletonema costatum against hemolysin protein of pathogenic bacteria Vibrio harveyi. The extract of S. costatum was tested against growth and virulence produced by V. harveyi during larviculture of Penaeus monodon. The extract was analyzed for phyto-constituents through GC-MS and used them as ligand molecule in docking. S. costatum extract at 200 µg mL-1 was found to decrease 35.20% of cumulative percentage mortality (CPM) in postlarvae of P. monodon against V. harveyi infections. The biomolecule Docasane, an alkane from the extract of S. costatum, exposed highest binding interaction than other compounds during docking analysis. The level of significance (P < 0.05) was found in CPM, growth, and virulence factors of V. harveyi studies. Thus, the present finding predicts that extract of S. costatum containing biomolecules can be recommended for use in the shrimp culture-based grow-out and hatchery units for eliminating bioluminescent V. harveyi.

Nanostructured Materials for Drug Delivery and Tissue Engineering Applications

Nanotechnology and nanostructured materials for drug delivery and tissue engineering applications are relatively new field that is constantly advancing and expanding. The materials used are at the nanoscale level. Recently, great discoveries and applications have been made (Agents for use in chemotherapy, biological agents and immunotherapy agents) in the treatment of diseases in various areas. Tissue engineering is based on the regeneration and repair of damaged organs and tissues by developing biological substitutes that restore, maintain or improve the function of tissues and organs. Cells isolated from patients are used to seed 3D nanoparticles that can be synthetic or natural biomaterials. For the development of new tissue in tissue engineering, it is necessary to meet the conditions for connecting cells. This paper will present the ways of connecting cells and creating new tissues. Some recent discoveries and advances in the field of nanomedicine and the application of nanotechnology in drug delivery will be presented. Furthermore, the improvement of the effectiveness of new and old drugs based on the application of nanotechnology will be shown.

An Overview of Polymeric Nanoparticles-Based Drug Delivery System in Cancer Treatment

Cancer is recognized as one of the world's deadliest diseases, with more than 10 million new cases each year. Over the past 2 decades, several studies have been performed on cancer to pursue solutions for effective treatment. One of the vital benefits of utilizing nanoparticles (NPs) in cancer treatment is their high adaptability for modification and amalgamation of different physicochemical properties to boost their anti-cancer activity. Various nanomaterials have been designed as nanocarriers attributing nontoxic and biocompatible drug delivery systems with improved bioactivity. The present review article briefly explained various types of nanocarriers, such as organic-inorganic-hybrid NPs, and their targeting mechanisms. Here a special focus is given to the synthesis, benefits, and applications of polymeric NPs (PNPs) involved in various anti-cancer therapeutics. It has also been discussed about the drug delivery approach by the functionalized/encapsulated PNPs (without/with targeting ability) that are being applied in the therapy and diagnostic (theranostics). Overall, this review can give a glimpse into every aspect of PNPs, from their synthesis to drug delivery application for cancer cells.

Multitarget Potential of Phytochemicals from Traditional Medicinal Tree, Terminalia arjuna (Roxb. ex DC.) Wight & Arnot as Potential Medicaments for Cardiovascular Disease: An In-Silico Approach

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Terminalia arjuna (Roxb. ex DC.) Wight & Arnot of the Combretaceae family is one of the most frequently approved and utilized medicinal trees in the traditional medicinal system, which was used for the treatment of a variety of diseases, including cardiovascular disorders. The present study aims to identify phytochemicals from T. arjuna, that do not exhibit any toxicity and have significant cardioprotective activity using an in-silico technique. Four different cardiovascular proteins, namely human angiotensin receptor (PDB ID: 4YAY), P38 mitogen-activated protein kinase (MAPK, PDB ID: 4DLI), 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-Co A) reductase (PDB ID: 1HW9), and human C-reactive protein (PDB ID: 1B09), were used as target proteins to identify potential inhibitors using a virtual screening of the phytochemicals in T. arjuna revealed casuarinin as a potential inhibitor of all selected target proteins with strong binding energy. Furthermore, MD simulations for a 100 ns time scale also revealed that most of the key protein contacts of all target proteins were retained throughout the simulation trajectories. Binding free energy calculations using the MM-GBSA approach also support a strong inhibitory effect of casuarinin on target proteins. Casuarinin's effective binding to these proteins lays the groundwork for the development of broad-spectrum drugs as well as the understanding of the underlying mechanism against cardiovascular diseases through in vivo and clinical studies.

TNF-α Inhibitors from Natural Compounds: An Overview, CADD Approaches, and their Exploration for Anti-inflammatory Agents

Inflammation is a natural process that occurs in the organism in response to harmful external agents. Despite being considered beneficial, exaggerated cases can cause severe problems for the body. The main inflammatory manifestations are pain, increased temperature, edema, decreased mobility, and quality of life for affected individuals. Diseases such as arthritis, cancer, allergies, infections, arteriosclerosis, neurodegenerative diseases, and metabolic problems are mainly characterized by an exaggerated inflammatory response. Inflammation is related to two categories of substances: pro- and anti-inflammatory mediators. Among the pro-inflammatory mediators is Tumor Necrosis Factor-α (TNF-α). It is associated with immune diseases, cancer, and psychiatric disorders which increase its excretion. Thus, it becomes a target widely used in discovering new antiinflammatory drugs. In this context, secondary metabolites biosynthesized by plants have been used for thousands of years and continue to be one of the primary sources of new drug scaffolds against inflammatory diseases. To decrease costs related to the drug discovery process, Computer-Aided Drug Design (CADD) techniques are broadly explored to increase the chances of success. In this review, the main natural compounds derived from alkaloids, flavonoids, terpene, and polyphenols as promising TNF-α inhibitors will be discussed. Finally, we applied a molecular modeling protocol involving all compounds described here, suggesting that their interactions with Tyr⁵⁹, Tyr¹¹⁹, Tyr¹⁵¹, Leu⁵⁷, and Gly¹²¹ residues are essential for the activity. Such findings can be useful for research groups worldwide to design new anti-inflammatory TNF-α inhibitors.

Molecular modeling of potent novel sulfonamide derivatives as non-peptide small molecule anti-COVID 19 agents

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19. The Sulfonamides groups have been widely introduced in several drugs, especially for their antibacterial activities and generally prescribed for respiratory infections. On the other hand, imidazole groups have the multipotency to act as drugs, including antiviral activity. We have used a structure-based drug design approach to design some imidazole derivatives of sulfonamide, which can efficiently bind to the active site of SARS-CoV-2 main protease and thus may have the potential to inhibit its proteases activity. We conducted molecular docking and molecular dynamics simulation to observe the stability and flexibility of inhibitor complexes. We have checked ADMET (absorption, distribution, metabolism, excretion and toxicity) and drug-likeness rules to scrutinize toxicity and then designed the most potent compound based on computational chemistry. Our small predicted molecule non-peptide protease inhibitors could provide a useful model in the further search for novel compounds since it has many advantages over peptidic drugs, like lower side effects, toxicity and less chance of drug resistance. Further, we confirmed the stability of our inhibitor-complex and interaction profile through the Molecular dynamics simulation study. Our small predicted moleculeCommunicated by Ramaswamy H. Sarma.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

Synthesis of acetamidosulfonamide derivatives with antioxidative and QSAR studies

A series of sixteen acetamidosulfonamide derivatives (1-16) have been synthesized and investigated for their antioxidant (radical scavenging and superoxide dismutase (SOD)) and antimicrobial activities. Most compounds exhibited antioxidant activities in which compound 15 displayed the most potent radical scavenging and SOD activities. Quantitative structure-activity relationship (QSAR) has been studied using multiple linear regression. The constructed QSAR models displayed high correlation coefficient (Q ² _LOO-CV = 0.9708 and 0.8753 for RSA and SOD activities, respectively), but low root mean square error (RMSE _LOO-CV = 0.5105 and 1.3571 for RSA and SOD activities, respectively). The structure-activity relationship showed that an ethylene group connected to pyridine ring provided significant antioxidant activities. The QSAR models give insight into the rational designed of eighty new sulfonamides with various electron donating and withdrawing groups. The top five new designed sulfonamides with nitro group are potential antioxidants to be further developed for medicinal applications.

Homology modelling of Trypanosoma brucei major surface proteases and molecular docking of variant surface glycoproteins and inhibitor ligands for drug design

Trypanosomes, which cause animal African trypanosomiasis, escape host immune responses by renewing their variable surface glycoprotein (VSG) coat. Chemotherapy is currently the only form of external intervention available. However, the efficacy of current trypanocides is poor due to overuse leading to an increase in drug resistance. Major surface proteases (MSPs) of trypanosomes, which are zinc-dependent metalloproteases, are possible drug targets. A Trypanosoma brucei MSP-B (TbMSP-B) mediates parasite antigenic variation via cleavage of 60% of VSG molecules. Whilst TbMSP-A has no apparent role in VSG cleavage; it is not known if TbMSP-C is involved in VSG cleavage. In this study, three-dimensional structures of TbMSP-A, TbMSP-B and TbMSP-C were modelled. By comparing the docking poses of the C-terminal domains of VSG substrates into the models, TbMSP-C showed an affinity for similar VSG substrate sites as TbMSP-B, but these sites differed from those recognised by TbMSP-A. This observation suggests that TbMSP-C may be involved in VSG cleavage during antigenic variation. Furthermore, by docking small inhibitor ligands into the TbMSP-B and TbMSP-C homology models, followed by molecular dynamics simulations, ligands with potential anti-trypanosomal activity were identified. Docking studies also revealed the depth of the S₁' pockets of TbMSP-B and TbMSP-C, which is influential in ligand and substrate binding, thereby identifying the protease subsite pocket that should be targeted in drug design.

mRNA - A game changer in regenerative medicine, cell-based therapy and reprogramming strategies

After thirty years of intensive research shaping and optimizing the technology, the approval of the first mRNA-based formulation by the EMA and FDA in order to stop the COVID-19 pandemic was a breakthrough in mRNA research. The astonishing success of these vaccines have brought the mRNA platform into the spotlight of the scientific community. The remarkable persistence of the groundwork is mainly attributed to the exceptional benefits of mRNA application, including the biological origin, immediate but transitory mechanism of action, non-integrative properties, safe and relatively simple manufacturing as well as the flexibility to produce any desired protein. Based on these advantages, a practical implementation of in vitro transcribed mRNA has been considered in most areas of medicine. In this review, we discuss the key preconditions for the rise of the mRNA in the medical field, including the unique structural and functional features of the mRNA molecule and its vehicles, which are crucial aspects for a production of potent mRNA-based therapeutics. Further, we focus on the utility of mRNA tools particularly in the scope of regenerative medicine, i.e. cell reprogramming approaches or manipulation strategies for targeted tissue restoration. Finally, we highlight the strong clinical potential but also the remaining hurdles to overcome for the mRNA-based regenerative therapy, which is only a few steps away from becoming a reality.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Guiding the Immune Response to a Conserved Epitope in MSP2, an Intrinsically Disordered Malaria Vaccine Candidate

The malaria vaccine candidate merozoite surface protein 2 (MSP2) has shown promise in clinical trials and is in part responsible for a reduction in parasite densities. However, strain-specific reductions in parasitaemia suggested that polymorphic regions of MSP2 are immuno-dominant. One strategy to bypass the hurdle of strain-specificity is to bias the immune response towards the conserved regions. Two mouse monoclonal antibodies, 4D11 and 9H4, recognise the conserved C-terminal region of MSP2. Although they bind overlapping epitopes, 4D11 reacts more strongly with native MSP2, suggesting that its epitope is more accessible on the parasite surface. In this study, a structure-based vaccine design approach was applied to the intrinsically disordered antigen, MSP2, using a crystal structure of 4D11 Fv in complex with its minimal binding epitope. Molecular dynamics simulations and surface plasmon resonance informed the design of a series of constrained peptides that mimicked the 4D11-bound epitope structure. These peptides were conjugated to keyhole limpet hemocyanin and used to immunise mice, with high to moderate antibody titres being generated in all groups. The specificities of antibody responses revealed that a single point mutation can focus the antibody response towards a more favourable epitope. This structure-based approach to peptide vaccine design may be useful not only for MSP2-based malaria vaccines, but also for other intrinsically disordered antigens.

Automation and data-driven design of polymer therapeutics

Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.

Mutations of Intrinsically Disordered Protein Regions Can Drive Cancer but Lack Therapeutic Strategies

Many proteins contain intrinsically disordered regions (IDRs) which carry out important functions without relying on a single well-defined conformation. IDRs are increasingly recognized as critical elements of regulatory networks and have been also associated with cancer. However, it is unknown whether mutations targeting IDRs represent a distinct class of driver events associated with specific molecular and system-level properties, cancer types and treatment options. Here, we used an integrative computational approach to explore the direct role of intrinsically disordered protein regions driving cancer. We showed that around 20% of cancer drivers are primarily targeted through a disordered region. These IDRs can function in multiple ways which are distinct from the functional mechanisms of ordered drivers. Disordered drivers play a central role in context-dependent interaction networks and are enriched in specific biological processes such as transcription, gene expression regulation and protein degradation. Furthermore, their modulation represents an alternative mechanism for the emergence of all known cancer hallmarks. Importantly, in certain cancer patients, mutations of disordered drivers represent key driving events. However, treatment options for such patients are currently severely limited. The presented study highlights a largely overlooked class of cancer drivers associated with specific cancer types that need novel therapeutic options.

Abiraterone and D4, 3-keto Abiraterone binding to CYP17A1, a structural comparison study by molecular dynamic simulation

The importance of computer-aided drug design and development is clear nowadays. These approaches smooth the way of designing some efficient candidates based on drugs in use. At this place, we studied the mechanism of D4-abiraterone (D4A), the active metabolite of Abiraterone (Abi), binding to CYP17A1 compared with Abi. The molecular dynamics simulation results reveal that the metabolite, which lacks the key 3β-OH group, has a varied H-bond forming pattern. The critical H-bond between 3β-OH of Abi with Asn_202 turns to 3 Keto-O of D4A with Arg_239 in the substrate-binding site. This interaction causes a remarkable distance of 0.63 nm between D4A nitrogen and Fe in heme, which reduces its 17,20 lyase selectivity. The D4A keto moiety presents an immense number of H-bond with surrounding solvent molecules compared with the Abi hydroxyl group. As a result, D4A develops a weaker H-bond network with the enzyme. Otherwise, the heterocyclic nature of inhibitors helps for noticeable van der Waals interaction formation with CYP17A1. However, Abi stabilized position in the binding site helps more van der Waals interactions deposition than D4A. These results convinced the importance of the conserved H-bond for acquiring the proper position by the substrate or inhibitor in the binding site.

Antioxidant and anti-sickling activity of glucal-based triazoles compounds - An in vitro and in silico study

The sickle cell disease (SCD) has a genetic cause, characterized by a replacement of glutamic acid to valine in the β-chain of hemoglobin. The disease has no effective treatment so far, and patients suffer a range from acute to chronic complications that include chronic hemolytic anemia, vaso-occlusive ischemia, pain, acute thoracic syndrome, cerebrovascular accident, nephropathy, osteonecrosis and reduced lifetime. The oxidation in certain regions of the hemoglobin favors the reactive oxygen species (ROS) formation, which is the cause of many clinical manifestations. Antioxidants have been studied to reduce the hemoglobin ROS levels, and in this sense, we have searched for new antioxidants glucal-based triazoles compounds with anti-sickling activity. Thirty analogues were synthetized and tested in in vitro antioxidant assays. Two of them were selected based in their effects and concentration-response activity and conducted to in cell assays. Both molecules did not cause any hemolysis and could reduce the red blood cell damage caused by hydrogen peroxide, in a model of oxidative stress induction that mimics the SCD. Moreover, one molecule (termed 11m), besides reducing the hemolysis, was able to prevent the cell damage caused by the hydrogen peroxide. Later on, by in silico pharmacokinetics analysis, we could see that 11m has appropriated proprieties for druggability and the probable mechanism of action is the binding to Peroxiredoxin-5, an antioxidant enzyme that reduces the hydrogen peroxide levels, verified after molecular docking assays. Thus, starting from 30 glucal-based triazoles molecules in a structure-activity relationship, we could select one with antioxidant proprieties that could act on RBC to reduce the oxidative stress, being useful for the treatment of SCD.

Molecular docking studies of phytocompounds of Rheum emodi Wall with proteins responsible for antibiotic resistance in bacterial and fungal pathogens: in silico approach to enhance the bio-availability of antibiotics

Rheum emodi Wall. (Himalayan rhubarb) has many pharmacological activities such as antioxidant, antimicrobial, antiviral, anticancer and wound healing. The present study was aimed to understand if major phytocompounds of Rheum emodi could bind proteins responsible for antibiotic resistance in bacterial and fungal pathogens and enhance the potency of antibiotics. The major phytocompounds of R. emodi (emodin, rhein-13c6 and chrysophenol dimethy ether) were retrieved from the Pubchem and target proteins were retrieved from RCSB protein data bank. The docking study was performed by using AutoDock vina software and Molinspiration, swiss ADME servers were used for the determination of Lipinski rule of 5, drug-likeness prediction respectively, whereas, admetSAR and Protox-II tools were used for toxicity prediction. To study the docking accuracy of protein-ligand complexes, MD simulation for 100 ns was done by using Desmond program version 2.0 (Academic version). Among all the selected phytocompounds, emodin showed the best binding affinity against bacterial (Penicillin binding protein 3, 3VSL and fungal target (cytochrome P450 14 alpha-sterol demethylase 1EA1) with binding energy -8.2 and -8.0 Kcal mol^-1 respectively. Similarly, rhein-13C6 showed the best binding affinity against fungal target (n-myristoyl transferase 1IYL) with binding energy -8.0 Kcal mol^-1 which is higher than antibacterial and antifungal antibiotics. All the selected phytocompounds also fulfill Lipinski rule, non-carcinogenic and non-cytotoxic in nature. These compounds also showed high LD₅₀ value showing non-toxicity of these phytocompounds. MD simulation studies of phytocompounds (emodin and rhein-13C6) define the stability of protein-ligand complexes with in 100 ns time scale.Communicated by Freddie R. Salsbury.

X-Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A2A Adenosine Receptor Antagonists

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X-ray crystallography to reveal the binding mode of an antagonist series to the A2A adenosine receptor (AR). Eight A2A AR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A2A AR were experimentally determined and investigated through a cycle of ligand-FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X-ray crystallography of the A2A AR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A2A AR, an emerging target in immuno-oncology.

Identification of Novel Phyto-chemicals from Ocimum basilicum for the Treatment of Parkinson's Disease using In Silico Approach

BACKGROUND

Parkinson's disease is characterized by decreased level of dopaminergic neurotransmitters and this decrease is due to the degradation of dopamine by protein Monoamine Oxidase B (MAO-B). In order to treat Parkinson's disease, MAO-B should be inhibited.

OBJECTIVE

To find out the novel phytochemicals from plant Ocimum basilicum that can inhibit MAO-B by using the in silico methods.

METHODS

The data of chemical constituents from plant Ocimum basilicum was collected and inhibitory activity of these phytochemicals was then predicted by using the Structure-Based (SB) and Ligand-Based Virtual Screening (LBVS) methods. Molecular docking, one of the common Structure-Based Virtual Screening method, has been used during this search. Traditionally, molecular docking is used to predict the orientation and binding affinity of the ligand within the active site of the protein. Molegro Virtual Docker (MVD) software has been used for this purpose. On the other hand, Random Forest Model, one of the LBVS method, has also been used to predict the activity of these chemical constituents of Ocimum basilicum against the MAO-B.

RESULTS

During the docking studies, all the 108 compounds found in Ocimum basilicum were docked within the active site of MAO-B (PDB code: 4A79) out of which, 57 compounds successfully formed the hydrogen bond with tyr 435, a crucial amino acid for the biological activity of the enzyme. Rutin (-182.976 Kcal/mol), Luteolin (-163.171 Kcal/mol), Eriodictyol-7-O-glucoside (- 160.13 Kcal/mol), Rosmarinic acid (-133.484 Kcal/mol) and Isoquercitrin (-131.493 Kcal/mol) are among the top hits with the highest MolDock score along with hydrogen interaction with tyr 435. Using the RF model, ten compounds out of 108 chemical constituent of Ocimum basilicum were predicted to be active, Apigenin (1.0), Eriodictyol (1.0), Orientin (0.876), Kaempferol (0.8536), Luteolin (0.813953) and Rosmarinic-Acid (0.7738095) are predicted to be most active with the highest RF score.

CONCLUSION

The comparison of the two screening methods show that the ten compounds that were predicted to be active by the RF model, are also found in top hits of docking studies with the highest score. The top hits obtained during this study are predicted to be the inhibitor of MAO-B, thus, could be used further for the development of drugs for the treatment of Parkinson's disease (PD).

BRIDGE: An Open Platform for Reproducible High-Throughput Free Energy Simulations

Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE) is an open-source web platform developed with the aim to provide an environment for the design of reliable methods to conduct reproducible biomolecular simulations. It is built on the well-known Galaxy bioinformatics platform. Through this, BRIDGE hosts computational chemistry tools on public web servers for internet use and provides machine- and operating-system-independent portability using the Docker container platform for local use. This construction improves the accessibility, shareability, and reproducibility of computational methods for molecular simulations. Here we present integrated free energy tools (or apps) to calculate absolute binding free energies (ABFEs) and relative binding free energies (RBFEs), as illustrated through use cases. We present free energy perturbation (FEP) methods contained in various software packages such as GROMACS and YANK that are independent of hardware configuration, software libraries, or operating systems when ported in the Galaxy-BRIDGE Docker container platform. By performing cyclin-dependent kinase 2 (CDK2) FEP calculations on geographically dispersed web servers (in Africa and Europe), we illustrate that large-scale computations can be performed using the exact same codes and methodology by collaborating groups through publicly shared protocols and workflows. The ease of public sharing and independent reproduction of simulations via BRIDGE makes possible an open review of methods and complete simulation protocols. This makes the discovery of inhibitors for drug targets accessible to nonexperts and the computer experiments that are used to arrive at leads verifiable by experts and reviewers. We illustrate this on β-galactoside α-2,3-sialyltransferase I (ST3Gal-I), a breast cancer drug target, where a combination of RBFE and ABFE methods are used to compute the binding free energies of three inhibitors.

The Advent of Generative Chemistry

Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

Strategies for targeting the cardiac sarcomere: avenues for novel drug discovery

Introduction: Heart failure remains one of the largest clinical challenges in the United States. Researchers have continually searched for more effective heart failure treatments that target the cardiac sarcomere but have found few successes despite numerous expensive cardiovascular clinical trials. Among many reasons, the high failure rate of cardiovascular clinical trials may be partly due to incomplete characterization of a drug candidate's complex interaction with cardiac physiology.Areas covered: In this review, the authors address the issue of preclinical cardiovascular studies of sarcomere-targeting heart failure therapies. The authors consider inherent tradeoffs made between mechanistic transparency and physiological fidelity for several relevant preclinical techniques at the atomic, molecular, heart muscle fiber, whole heart, and whole-organism levels. Thus, the authors suggest a comprehensive, bottom-up approach to preclinical cardiovascular studies that fosters scientific rigor and hypothesis-driven drug discovery.Expert opinion: In the authors' opinion, the implementation of hypothesis-driven drug discovery practices, such as the bottom-up approach to preclinical cardiovascular studies, will be imperative for the successful development of novel heart failure treatments. However, additional changes to clinical definitions of heart failure and current drug discovery culture must accompany the bottom-up approach to maximize the effectiveness of hypothesis-driven drug discovery.

A computational approach yields selective inhibitors of human excitatory amino acid transporter 2 (EAAT2)

Excitatory amino acid transporters (EAATs) represent a protein family that is an emerging drug target with great therapeutic potential for managing central nervous system disorders characterized by dysregulation of glutamatergic neurotransmission. As such, it is of significant interest to discover selective modulators of EAAT2 function. Here, we applied computational methods to identify specific EAAT2 inhibitors. Utilizing a homology model of human EAAT2, we identified a binding pocket at the interface of the transport and trimerization domain. We next conducted a high-throughput virtual screen against this site and identified a selective class of EAAT2 inhibitors that were tested in glutamate uptake and whole-cell electrophysiology assays. These compounds represent potentially useful pharmacological tools suitable for further exploration of the therapeutic potential of EAAT2 and may provide molecular insights into mechanisms of allosteric modulation for glutamate transporters.

IODVA1, a guanidinobenzimidazole derivative, targets Rac activity and Ras-driven cancer models

We report the synthesis and preliminary characterization of IODVA1, a potent small molecule that is active in xenograft mouse models of Ras-driven lung and breast cancers. In an effort to inhibit oncogenic Ras signaling, we combined in silico screening with inhibition of proliferation and colony formation of Ras-driven cells. NSC124205 fulfilled all criteria. HPLC analysis revealed that NSC124205 was a mixture of at least three compounds, from which IODVA1 was determined to be the active component. IODVA1 decreased 2D and 3D cell proliferation, cell spreading and ruffle and lamellipodia formation through downregulation of Rac activity. IODVA1 significantly impaired xenograft tumor growth of Ras-driven cancer cells with no observable toxicity. Immuno-histochemistry analysis of tumor sections suggests that cell death occurs by increased apoptosis. Our data suggest that IODVA1 targets Rac signaling to induce death of Ras-transformed cells. Therefore, IODVA1 holds promise as an anti-tumor therapeutic agent.

Hydrogen-Deuterium Exchange Mass-Spectrometry of Secondary Active Transporters: From Structural Dynamics to Molecular Mechanisms

Membrane transporters allow the selective transport of otherwise poorly permeable solutes across the cell membrane and thus, play a key role in maintaining cellular homeostasis in all kingdoms of life. Importantly, these proteins also serve as important drug targets. Over the last decades, major progress in structural biology methods has elucidated important structure-function relationships in membrane transporters. However, structures obtained using methods such as X-ray crystallography and high-resolution cryogenic electron microscopy merely provide static snapshots of an intrinsically dynamic, multi-step transport process. Therefore, there is a growing need for developing new experimental approaches capable of exploiting the data obtained from the high-resolution snapshots in order to investigate the dynamic features of membrane proteins. Here, we present the basic principles of hydrogen-deuterium exchange mass-spectrometry (HDX-MS) and recent advancements in its use to study membrane transporters. In HDX-MS experiments, minute amounts of a protein sample can be used to investigate its structural dynamics under native conditions, without the need for chemical labelling and with practically no limit on the protein size. Thus, HDX-MS is instrumental for resolving the structure-dynamic landscapes of membrane proteins in their apo (ligand-free) and ligand-bound forms, shedding light on the molecular mechanism underlying the transport process and drug binding.

Theoretical and Experimental Approaches Aimed at Drug Design Targeting Neurodegenerative Diseases

In recent years, green chemistry has been strengthening, showing how basic and applied sciences advance globally, protecting the environment and human health. A clear example of this evolution is the synergy that now exists between theoretical and computational methods to design new drugs in the most efficient possible way, using the minimum of reagents and obtaining the maximum yield. The development of compounds with potential therapeutic activity against multiple targets associated with neurodegenerative diseases/disorders (NDD) such as Alzheimer’s disease is a hot topic in medical chemistry, where different scientists from various disciplines collaborate to find safe, active, and effective drugs. NDD are a public health problem, affecting mainly the population over 60 years old. To generate significant progress in the pharmacological treatment of NDD, it is necessary to employ different experimental strategies of green chemistry, medical chemistry, and molecular biology, coupled with computational and theoretical approaches such as molecular simulations and chemoinformatics, all framed in the rational drug design targeting NDD. Here, we review how green chemistry and computational approaches have been used to develop new compounds with the potential application against NDD, as well as the challenges and new directions of the drug development multidisciplinary process.

A Dual Wavelength Polymerization and Bioconjugation Strategy for High Throughput Synthesis of Multivalent Ligands

Structure-function relationships for multivalent polymer scaffolds are highly complex due to the wide diversity of architectures offered by such macromolecules. Evaluation of this landscape has traditionally been accomplished case-by-case due to the experimental difficulty associated with making these complex conjugates. Here, we introduce a simple dual-wavelength, two-step polymerize and click approach for making combinatorial conjugate libraries. It proceeds by incorporation of a polymerization friendly cyclopropenone-masked dibenzocyclooctyne into the side chain of linear polymers or the α-chain end of star polymers. Polymerizations are performed under visible light using an oxygen tolerant porphyrin-catalyzed photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) process, after which the deprotection and click reaction is triggered by UV light. Using this approach, we are able to precisely control the valency and position of ligands on a polymer scaffold in a manner conducive to high throughput synthesis.

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Modelling the binding mode of macrocycles: Docking and conformational sampling

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.

Protein surface topography as a tool to enhance the selective activity of a potassium channel blocker

Tk-hefu is an artificial peptide designed based on the α-hairpinin scaffold, which selectively blocks voltage-gated potassium channels K_v1.3. Here we present its spatial structure resolved by NMR spectroscopy and analyze its interaction with channels using computer modeling. We apply protein surface topography to suggest mutations and increase Tk-hefu affinity to the K_v1.3 channel isoform. We redesign the functional surface of Tk-hefu to better match the respective surface of the channel pore vestibule. The resulting peptide Tk-hefu-2 retains K_v1.3 selectivity and displays ∼15 times greater activity compared with Tk-hefu. We verify the mode of Tk-hefu-2 binding to the channel outer vestibule experimentally by site-directed mutagenesis. We argue that scaffold engineering aided by protein surface topography represents a reliable tool for design and optimization of specific ion channel ligands.

Molecular Imaging in Drug Discovery and Development

Noninvasive imaging has played an increasing role in the process of cardiovascular drug development. This review focuses specifically on the use of molecular imaging, which has been increasingly applied to improve and accelerate certain preclinical steps in drug development, including the identification of appropriate therapeutic targets, evaluation of on-target and off-target effects of candidate therapies, assessment of dose response, and the evaluation of drug or biological biodistribution and pharmacodynamics. Unlike the case in cancer medicine, in cardiovascular medicine, molecular imaging has not been used as a primary surrogate clinical end point for drug approval. However, molecular imaging has been applied in early clinical trials, particularly in phase 0 studies, to demonstrate proof-of-concept or to explain variation in treatment effect. Many of these applications where molecular imaging has been used in drug development have involved the retasking of technologies that were originally intended as clinical diagnostics. With greater experience and recognition of the rich information provided by in vivo molecular imaging, it is anticipated that it will increasingly be used to address the enormous time and costs associated with bringing a new drug to clinical launch.

Atom Probe Tomography for 3D Structural and Chemical Analysis of Individual Proteins

Determination of the 3D structure of proteins and other biomolecules is a major goal in structural biology, to provide insights to their biological function. Such structures are historically unveiled experimentally by X-ray crystallography or NMR spectroscopy, and in recent years using cryo-electron microscopy. Here, a method for structural analysis of individual proteins on the sub-nanometer scale using atom probe tomography is described. This technique offers a combination of high-resolution analysis of biomolecules in 3D, and the chemical sensitivity of mass spectrometry. As a model protein, the well-characterized antibody IgG is used. IgG is encapsulated in an amorphous solid silica matrix via a sol-gel process to provide the requisite support for atom probe analysis. The silica synthesis is tuned to resemble physiological conditions. The 3D reconstructions show good agreement with the protein databank IgG crystal structure. This suggests that the silica-embedding strategy can open the field of atom probe tomography to the analysis of biological molecules. In addition to high-resolution structural information, the technique may potentially provide chemical information on the atomic scale using isotopic labeling. It is envisaged that this method may constitute a useful complement to existing tools in structural biology, particularly for the examination of proteins with low propensity for crystallization.

Prediction of Ligands Binding Acetylcholinesterase with Potential Antidotal Activity: A Virtual Screening Approach

Intoxications caused by organophosphorus compounds (OPs) are associated with the reversible, and sometimes irreversible interaction with acetylcholinesterase (AChE). OPs are commonly used as pesticides mainly in developing countries, where the associated poisoning is a major health problem related to suicidal attempts, careless manipulation, and chemical warfare. The current antidotes are oxime-based drugs that can regenerate the AChE catalytic activity. Nevertheless, challenges associated with lack of efficiency and difficulties for crossing blood-brain barrier have motivated the design of novel alternatives. We used a validated molecular docking approach for the virtual screening of 579,890 synthetic ligands and 478 drugs against a human AChE in its apo conformation, and a murine AChE conjugated with the OP tabun. After filtering, 7 hits were selected as potential competitors due to the formation of key interactions within the active site gorge of the AChE structure, and potential reactivators based on interactions with amino acids of the catalytic triad in the presence of organophosphorus compounds. The selected candidates will be further evaluated through in vitro and in vivo assays.