The Critical Role of Passive Permeability in Designing Successful Drugs

Multifunctional in vitro, in silico and DFT analyses on antimicrobial BagremycinA biosynthesized by Micromonospora chokoriensis CR3 from Hieracium canadense

Among the actinomycetes in the rare genera, Micromonospora is of great interest since it has been shown to produce novel therapeutic compounds. Particular emphasis is now on its isolation from plants since its population from soil has been extensively explored. The strain CR3 was isolated as an endophyte from the roots of Hieracium canadense, and it was identified as Micromonospora chokoriensis through 16S gene sequencing and phylogenetic analysis. The in-vitro analysis of its extract revealed it to be active against the clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) and Candida tropicalis (15 mm). No bioactivity was observed against Gram-negative bacteria, Escherichia coli ATCC 25922, and Klebsiella pneumoniae ATCC 706003. The Micromonospora chokoriensis CR3 extract was also analyzed through the HPLC-DAD-UV-VIS resident database, and it gave a maximum match factor of 997.334 with the specialized metabolite BagremycinA (BagA). The in-silico analysis indicated that BagA strongly interacted with the active site residues of the sterol 14-α demethylase and thymidylate kinase enzymes, with the lowest binding energies of - 9.7 and - 8.3 kcal/mol, respectively. Furthermore, the normal mode analysis indicated that the interaction between these proteins and BagA was stable. The DFT quantum chemical properties depicted BagA to be reasonably reactive with a HOMO-LUMO gap of (ΔE) of 4.390 eV. BagA also passed the drug-likeness test with a synthetic accessibility score of 2.06, whereas Protox-II classified it as a class V toxicity compound with high LD50 of 2644 mg/kg. The current study reports an endophytic actinomycete, M. chokoriensis, associated with H. canadense producing the bioactive metabolite BagA with promising antimicrobial activity, which can be further modified and developed into a safe antimicrobial drug.

Dual Strategy to Design New Agents Targeting Schistosoma mansoni: Advancing Phenotypic and SmCB1 Inhibitors for Improved Efficacy

In this study, we have identified and optimized two lead structures from an in-house screening, with promising results against the parasitic flatworm Schistosoma mansoni and its target protease S. mansoni cathepsin B1 (SmCB1). Our correlation analysis highlighted the significance of physicochemical properties for the compounds' in vitro activities, resulting in a dual approach to optimize the lead structures, regarding both phenotypic effects in S. mansoni newly transformed schistosomula (NTS), adult worms, and SmCB1 inhibition. The optimized compounds from both approaches ("phenotypic" vs "SmCB1" approach) demonstrated improved efficacy against S. mansoni NTS and adult worms, with 2h from the "SmCB1" approach emerging as the most potent compound. 2h displayed nanomolar inhibition of SmCB1 (Ki = 0.050 μM) while maintaining selectivity toward human off-target cathepsins. Additionally, the greatly improved efficacy of compound 2h toward S. mansoni adults (86% dead worms at 10 μM, 68% at 1 μM, 35% at 0.1 μM) demonstrates its potential as a new therapeutic agent for schistosomiasis, underlined by its improved permeability.

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/.

Discovery of HC-7366: An Orally Bioavailable and Efficacious GCN2 Kinase Activator

A series of activators of GCN2 (general control nonderepressible 2) kinase have been developed, leading to HC-7366, which has entered the clinic as an antitumor therapy. Optimization resulted in improved permeability compared to that of the original indazole hinge binding scaffold, while maintaining potency at GCN2 and selectivity over PERK (protein kinase RNA-like endoplasmic reticulum kinase). The improved ADME properties of this series led to robust in vivo compound exposure in both rats and mice, allowing HC-7366 to be dosed in xenograft models, demonstrating that activation of the GCN2 pathway by this compound leads to tumor growth inhibition.

Predicting the intrinsic membrane permeability of Caco-2/MDCK cells by the solubility-diffusion model

Membrane permeability is one of the main determinants for the absorption, distribution, metabolism and excretion of compounds and is therefore of crucial importance for successful drug development. Experiments with artificial phospholipid membranes have shown that the intrinsic membrane permeability (P0) of compounds is well-predicted by the solubility-diffusion model (SDM). However, using the solubility-diffusion model to predict the P0 of biological Caco-2 and MDCK cell membranes has proven unreliable so far. Recent publications revealed that many published P0 extracted from Caco-2 and MDCK experiments are incorrect. In this work, we therefore used a small self-generated set as well as a large revised set of experimental Caco-2 and MDCK data from literature to compare experimental and predicted P0. The P0 extracted from Caco-2 and MDCK experiments were systematically lower than the P0 predicted by the solubility-diffusion model. However, using the following correlation: log P0,Caco-2/MDCK = 0.84 log P0,SDM - 1.85, P0 of biological Caco-2 and MDCK cell membranes was well-predicted by the solubility-diffusion model.

In situ setup for screening of drug permeation by NMR spectroscopy

There are various commercially available setups for studying drug permeation, which differ in cost and manual labor. We explore an artificial membrane in an NMR tube to assess drug permeation with automated measurements. NMR-based concentrations were validated with HPLC and compared to a conventional setup. Setup-specific challenges and workarounds as well as future setup-designs for this and other applications are discussed.

Early evaluation of opportunities in oral delivery of PROTACs to overcome their molecular challenges

PROteolysis TArgeting Chimeras (PROTACs) offer new opportunities in modern medicine by targeting proteins that are intractable to classic inhibitors. Heterobifunctional in nature, PROTACs are small molecules that offer a unique mechanism of protein degradation by hijacking the ubiquitin-mediated protein degradation pathway, known as the ubiquitin-proteasome system. Herein, we present an analysis on the structural characteristics of this novel chemical modality. Furthermore, we review and discuss the formulation opportunities to overcome the oral delivery challenges of PROTACs in drug discovery.

Molecular chameleons in drug discovery

Molecular chameleons possess a flexibility that allows them to dynamically shield or expose polar functionalities in response to the properties of the environment. Although the concept of molecular chameleons was introduced already in 1970, interest in them has grown considerably since the 2010s, when drug discovery has focused to an increased extent on new chemical modalities. Such modalities include cyclic peptides, macrocycles and proteolysis-targeting chimeras, all of which reside in a chemical space far from that of traditional small-molecule drugs. Both cell permeability and aqueous solubility are required for the oral absorption of drugs. Engineering these properties, and potent target binding, into the larger new modalities is a more daunting task than for traditional small-molecule drugs. The ability of chameleons to adapt to different environments may be essential for success. In this Review, we provide both general and theoretical insights into the realm of molecular chameleons. We discuss why chameleons have come into fashion and provide a do-it-yourself toolbox for their design; we then provide a glimpse of how advanced in silico methods can support molecular chameleon design.

Unraveling the therapeutic potential of Senna singueana phytochemicals to attenuate pancreatic cancer using protein-protein interactions, molecular docking, and MD simulation

Pancreatic cancer (PC) presents challenges due to limited treatment options and patients seek complementary therapies alongside conventional treatments to improve well-being. This study uses computational drug discovery approaches to find potential phytochemicals from S. singueana for PC treatment. Among the 38 phytochemicals screened from S. singueana, specific inhibitors against PC were selected. Protein-protein interaction (PPI) network analysis highlighted key targets with high degrees, including PTEN (8) and PTK2 (7) genes, along with their respective proteins 5BZX and 3BZ3, which were employed for molecular docking studies. 1-methylchrysene and 3-methyl-1,8,9-anthracenetriol showed strong binding affinities of - 9.2 and - 8.1 Kcal/mol, respectively. Molecular dynamics simulations lasting 300 ns assessed structural stability and interaction energy of compound-target dockings: 1-methylchrysene-PTEN and 3-methyl-1,8,9-anthracenetriol-PTK2. In molecular dynamics simulations, the 3-methyl-1,8,9-anthracenetriol-PTK2 complex showed lower RMSD, RMSF, radius of gyration, solvent-accessible surface area, and more hydrogen bonds than the 1-methylchrysene-PTEN complex. The 3-methyl-1,8,9-anthracenetriol-PTK2 complex exhibited significantly stronger binding with a binding free energy (ΔG) of - 21.92 kcal/mol compared to the less favourable ΔG of - 10.65 kcal/mol for the 1-methylchrysene-PTEN complex. The consistent and stable binding interaction observed in the 3-methyl-1,8,9-anthracenetriol-PTK2 complex highlights its potential as a potent inhibitor of Focal Adhesion Kinase 1. Consequently, it emerges as a promising lead compound for the development of pancreatic cancer therapeutics. Conversely, the fluctuations observed in the 1-methylchrysene-PTEN complex indicate a less stable binding interaction. This indicates the potential of 3-methyl-1,8,9-anthracenetriol as a primary candidate for pancreatic cancer treatment. These findings improve our grasp of S. singueana's multi-target effects and its promise in addressing pancreatic cancer. Nevertheless, additional in-vivo and in-vitro studies are required to validate their effectiveness and therapeutic potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00179-9.

Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates

Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, ∼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated in vitro PAMPA-BBB data with in vivo brain permeation data in rodents to observe a categorical correlation of 77%, suggesting that models developed using data from PAMPA-BBB can forecast in vivo brain permeability. Given that majority of prior research has relied on in vitro or in vivo data for assessing BBB permeability, our model, developed using the largest PAMPA-BBB dataset to date, offers an orthogonal means to estimate BBB permeability of small molecules. We deposited a subset of our data into PubChem bioassay database (AID: 1845228) and deployed the best performing model on the NCATS Open Data ADME portal (https://opendata.ncats.nih.gov/adme/). These initiatives were undertaken with the aim of providing valuable resources for the drug discovery community.

Assessment of the hypoglycemic and anti-hemostasis effects of Paederia foetida (L.) in controlling diabetes and thrombophilia combining in vivo and computational analysis

Paederia foetida is valued for its folk medicinal properties. This research aimed to assess the acute toxicity, hypoglycemic and anti-hemostasis properties of the methanolic extract of P. foetida leaves (PFLE). Acute toxicity of PFLE was performed on a mice model. Hypoglycemic and anti-hemostasis properties of PFLE were investigated on normal and streptozotocin-induced mice models. Deep learning, molecular docking, density functional theory, and molecular simulation techniques were employed to understand the underlying mechanisms through in silico study. Oral administration of PFLE at a dosage of 300 µg/kg body weight (BW) showed no signs of toxicity. Treatment with PFLE (300 µg/kg/BW) for 14 days resulted in a hypoglycemic condition and a 30.47% increase in body weight. Additionally, PFLE mixed with blood exhibited a 44.6% anti-hemostasis effect. Deep learning predicted the inhibitory concentration (pIC50, nM) of Cleomiscosins against SGLT2 and FXa to be 7.478 and 6.017, respectively. Molecular docking analysis revealed strong binding interactions of Cleomiscosins with crucial residues of the target proteins, exhibiting binding energies of -8.2 kcal/mol and -7.1 kcal/mol, respectively. ADME/Tox predictions indicated favorable pharmacokinetic properties of Cleomiscosins, and DFT calculations of frontier molecular orbitals analyzed the stability and reactivity of these compounds. Molecular simulation dynamics, principal component analysis and MM-PBSA calculation demonstrated the stable, compact, and rigid nature of the protein-ligand complexes. The methanolic PFLE exhibited significant hypoglycemic and anti-hemostasis properties. Cleomiscosin may have inhibitory properties for the development of novel drugs to manage diabetes and thrombophilia in the near future.

Plasmepsin II inhibitory potential of phytochemicals isolated from African antimalarial plants: a computational approach

Plamepsin II has been identified as a therapeutic target in the Plasmodium falciparum's life cycle and may lead to a drastic reduction in deaths caused by malaria worldwide. Africa flora is rich in medicinal qualities and possesses both simple and complex bioactive phytochemicals. This study utilized computational approaches like molecular docking, molecular dynamics simulation, quantum chemical calculations and ADMET to evaluate the plasmepsin II inhibitory properties of phytochemicals isolated from African antimalarial plants. Molecular docking was carried out to estimate the binding affinity of 229 phytochemicals whereby ekeberin A, dichamanetin, 10-hydroxyusambaresine, chamuvaritin and diuvaretin were selected. Further, RMSD and RMSF plots from the 100 ns simulation results showed that the screened phytochemicals were stable in the enzyme's binding pocket. The quantum chemical calculation revealed that all the phytochemicals are strong electrophiles, while ekeberin A was identified as the most stable and dichamanetin as the most reactive. Also, ADMET studies established the drug candidacy of the phytochemicals. Thus, these phytochemicals could act as good antimalarial agents after extensive in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

Current screening, design, and delivery approaches to address low permeability of chemically synthesized modalities in drug discovery and early clinical development

A drug's permeability across biological membranes is a key property associated with the successful development of an orally absorbed drug candidate. Although a variety of methods are available for predicting and assessing permeability, some are more preferred than others at specific stages of drug discovery and development across the pharmaceutical industry. Permeability measurements may be interpreted differently depending on the chosen method. Herein, we present a refreshed perspective on the screening approaches and philosophy in permeability evaluation, from early drug discovery to early clinical development. Additionally, we review and discuss chemical design and drug delivery technologies that can be leveraged to overcome permeability challenges, which are increasingly being used with emerging modalities.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Herbal concoction Unveiled: A computational analysis of phytochemicals' pharmacokinetic and toxicological profiles using novel approach methodologies (NAMs)

Herbal medications have an extensive history of use in treating various diseases, attributed to their perceived efficacy and safety. Traditional medicine practitioners and contemporary healthcare providers have shown particular interest in herbal syrups, especially for respiratory illnesses associated with the SARS-CoV-2 virus. However, the current understanding of the pharmacokinetic and toxicological properties of phytochemicals in these herbal mixtures is limited. This study presents a comprehensive computational analysis utilizing novel approach methodologies (NAMs) to investigate the pharmacokinetic and toxicological profiles of phytochemicals in herbal syrup, leveraging in-silico techniques and prediction tools such as PubChem, SwissADME, and Molsoft's database. Although molecular dynamics, docking, and broader system-wide analyses were not considered, future studies hold potential for further investigation in these areas. By combining drug-likeness with molecular simulation, researchers identify diverse phytochemicals suitable for complex medication development examining their pharmacokinetic-toxicological profiles in phytopharmaceutical syrup. The study focuses on herbal solutions for respiratory infections, with the goal of adding to the pool of all-natural treatments for such ailments. This research has the potential to revolutionize environmental and alternative medicine by leveraging in-silico models and innovative analytical techniques to identify novel phytochemicals with enhanced therapeutic benefits and explore network-based and systems biology approaches for a deeper understanding of their interactions with biological systems. Overall, our study offers valuable insights into the computational analysis of the pharmacokinetic and toxicological profiles of herbal concoction. This paves the way for advancements in environmental and alternative medicine. However, we acknowledge the need for future studies to address the aforementioned topics that were not adequately covered in this research.

Novel 1,2,4-oxadiazole compounds as PPAR-α ligand agonists: a new strategy for the design of antitumour compounds

Modulation of PPAR-α by natural ligands is a novel strategy for the development of anticancer therapies. A series of 16 compounds based on the structure of 3-(pyridin-3-yl)-5-(thiophen-3-yl)-1,2,4-oxadiazole (natural compound) with antitumour potential were designed and synthesised. The cytotoxicity and PPAR agonist activity of these synthetic 1,2,4-oxadiazoles were evaluated in the A-498 and DU 145 tumour cell lines. Preliminary biological evaluation showed that most of these synthetic 1,2,4-oxadiazoles are less cytotoxic (sulforhodamine B assay) than the positive control WY-14643. Regarding the PPAR-α modulation, compound 16 was the most active, with EC50 = 0.23-0.83 μM (PPAR-α). Additionally, compound 16 had a similar activity to the natural compound (EC50 = 0.18-0.77 μM) and was less toxic in the RPTEC and WPMY-1 cell lines (non-tumour cells) (CC50 = 81.66-92.67 μM) than the natural compound. Looking at the link between chemical structure and activity, our study demonstrates that changes to the natural 1,2,4-oxadiazole at the level of the thiophenyl residue can lead to new agonists of PPAR-α with promising anti-tumour activity.

Today's drug discovery and the shadow of the rule of 5

INTRODUCTION

The rule of 5 developed by Lipinski et al., a landmark and prescient piece of scholarship, focused the minds of drug hunters by systematically characterizing the physical make-up of drug molecules for the first time, noting many sub-optimal compounds identified by high-throughput screening practices. Its profound influence on thinking and practices, whilst providing benefit, perhaps etched the guidelines too strongly in the minds of some drug hunters who applied the bounds too literally without understanding the implications of the underlying statistics.

AREAS COVERED

This opinion is based on recent key developments that take thinking, measurements, and standards beyond those first set out, particularly the influences of molecular weight and the understanding, measurement, and calculation of lipophilicity.

EXPERT OPINION

Techniques and technologies for physicochemical estimations set new standards. It is timely to celebrate the significance and influence of the rule of 5, whilst taking thinking to new levels with better characterizations. The shadow of the rule of 5 may be long, but it is not dark, as new measurements, predictions and principles emerge as guiding lights in the design and prioritization of higher-quality molecules redefining the meaning of beyond the rule of 5.

Challenges and Opportunities in the Oral Delivery of Recombinant Biologics

Recombinant biological molecules are at the cutting-edge of biomedical research thanks to the significant progress made in biotechnology and a better understanding of subcellular processes implicated in several diseases. Given their ability to induce a potent response, these molecules are becoming the drugs of choice for multiple pathologies. However, unlike conventional drugs which are mostly ingested, the majority of biologics are currently administered parenterally. Therefore, to improve their limited bioavailability when delivered orally, the scientific community has devoted tremendous efforts to develop accurate cell- and tissue-based models that allow for the determination of their capacity to cross the intestinal mucosa. Furthermore, several promising approaches have been imagined to enhance the intestinal permeability and stability of recombinant biological molecules. This review summarizes the main physiological barriers to the oral delivery of biologics. Several preclinical in vitro and ex vivo models currently used to assess permeability are also presented. Finally, the multiple strategies explored to address the challenges of administering biotherapeutics orally are described.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Trends in Molecular Properties, Bioavailability, and Permeability across the Bayer Compound Collection

For oral drugs, medicinal chemists aim to design compounds with high oral bioavailability, of which permeability is a key determinant. Taking advantage of >2000 compounds tested in rat bioavailability studies and >20,000 compounds tested in Caco2 assays at Bayer, we have examined the molecular properties governing bioavailability and permeability. In addition to classical parameters such as logD and molecular weight, we also investigated the relationship between calculated pK_a and permeability. We find that neutral compounds retain permeability up to a molecular weight limit of 700, while stronger acids and bases are restricted to weights of 400-500. We also investigate trends for common properties such as hydrogen bond donors and acceptors, polar surface area, aromatic ring count, and rotatable bonds, including compounds which exceed Lipinski's rule of five (Ro5). These property-structure relationships are combined to provide design guidelines for bioavailable drugs in both traditional and "beyond rule of 5" (bRo5) chemical space.

Path sampling with memory reduction and replica exchange to reach long permeation timescales

Assessing kinetics in biological processes with molecular dynamics simulations remains a computational and conceptual challenge, given the large time and length scales involved. For kinetic transport of biochemical compounds or drug molecules, the permeability through the phospholipid membranes is a key kinetic property, but long timescales are hindering the accurate computation. Technological advances in high-performance computing therefore need to be accompanied by theoretical and methodological developments. In this contribution, the replica exchange transition interface sampling (RETIS) methodology is shown to give perspective toward observing longer permeation pathways. It is first reviewed how RETIS, a path-sampling methodology that gives in principle exact kinetics, can be used to compute membrane permeability. Next, recent and current developments in three RETIS aspects are discussed: several new Monte Carlo moves in the path-sampling algorithm, memory reduction by reducing pathlengths, and exploitation of parallel computing with CPU-imbalanced replicas. Finally, the memory reduction presenting a new replica exchange implementation, coined REPPTIS, is showcased with a permeant needing to pass a membrane with two permeation channels, either representing an entropic or energetic barrier. The REPPTIS results showed clearly that inclusion of some memory and enhancing ergodic sampling via replica exchange moves are both necessary to obtain correct permeability estimates. In an additional example, ibuprofen permeation through a dipalmitoylphosphatidylcholine membrane was modeled. REPPTIS succeeded in estimating the permeability of this amphiphilic drug molecule with metastable states along the permeation pathway. In conclusion, the presented methodological advances allow for deeper insight into membrane biophysics even if the pathways are slow, as RETIS and REPPTIS push the permeability calculations to longer timescales.

Commercially Available Cell-Free Permeability Tests for Industrial Drug Development: Increased Sustainability through Reduction of In Vivo Studies

Replacing in vivo with in vitro studies can increase sustainability in the development of medicines. This principle has already been applied in the biowaiver approach based on the biopharmaceutical classification system, BCS. A biowaiver is a regulatory process in which a drug is approved based on evidence of in vitro equivalence, i.e., a dissolution test, rather than on in vivo bioequivalence. Currently biowaivers can only be granted for highly water-soluble drugs, i.e., BCS class I/III drugs. When evaluating poorly soluble drugs, i.e., BCS class II/IV drugs, in vitro dissolution testing has proved to be inadequate for predicting in vivo drug performance due to the lack of permeability interpretation. The aim of this review was to provide solid proofs that at least two commercially available cell-free in vitro assays, namely, the parallel artificial membrane permeability assay, PAMPA, and the PermeaPad^® assay, PermeaPad, in different formats and set-ups, have the potential to reduce and replace in vivo testing to some extent, thus increasing sustainability in drug development. Based on the literature review presented here, we suggest that these assays should be implemented as alternatives to (1) more energy-intense in vitro methods, e.g., refining/replacing cell-based permeability assays, and (2) in vivo studies, e.g., reducing the number of pharmacokinetic studies conducted on animals and humans. For this to happen, a new and modern legislative framework for drug approval is required.

Going Viral: An Investigation into the Chameleonic Behaviour of Antiviral Compounds

The ability to adjust conformations in response to the polarity of the environment, i.e. molecular chameleonicity, is considered to be important for conferring both high aqueous solubility and high cell permeability to drugs in chemical space beyond Lipinski's rule of 5. We determined the conformational ensembles populated by the antiviral drugs asunaprevir, simeprevir, atazanavir and daclatasvir in polar (DMSO-d₆ ) and non-polar (chloroform) environments with NMR spectroscopy. Daclatasvir was fairly rigid, whereas the first three showed large flexibility in both environments, that translated into major differences in solvent accessible 3D polar surface area within each conformational ensemble. No significant differences in size and polar surface area were observed between the DMSO-d₆ and chloroform ensembles of these three drugs. We propose that such flexible compounds are characterized as "partial molecular chameleons" and hypothesize that their ability to adopt conformations with low polar surface area contributes to their membrane permeability and oral absorption.

Nα -Methylation of arginine: Implications for cell-penetrating peptides

The field of cell-penetrating peptides is dominated by the use of oligomers of arginine residues. Octanol-water partitioning in the presence of an anionic lipid is a validated proxy for cell-penetrative efficacy. Here, we add one, two, or three N-methyl groups to Ac-Arg-NH₂ and examine the effects on octanol-water partitioning. In the absence of an anionic lipid, none of these arginine derivatives can be detected in the octanol layer. In the presence of sodium dodecanoate, however, increasing N-methylation correlates with increasing partitioning into octanol, which is predictive of higher cell-penetrative ability. We then evaluated fully N^α -methylated oligoarginine peptides and observed an increase in their cellular penetration compared with canonical oligoarginine peptides in some contexts. These findings indicate that a simple modification, N^α -methylation, can enhance the performance of cell-penetrating peptides.

Advancing New Chemical Modalities into Clinical Studies

Drug discovery and development has experienced an incredible paradigm shift in the past two decades. What once was considered a predominant R&D landscape of small molecules within a prescribed properties and mechanism space now includes an innovative wave of new chemical modalities. Scientists in the pharmaceutical industry can now strategize across a variety of modalities to find the best option to modulate a given target and provide treatment for a specific disease. We have witnessed a remarkable change not only in molecular design but also in creative approaches to drug delivery that have enabled advancement of novel modalities to clinical studies. In this Microperspective, we evaluate the critical differences between traditional small molecules and beyond rule of 5 compounds, peptides, oligonucleotides, and biologics for advancing into development, particularly their pharmacokinetic profiles and drug delivery strategies.

Multipronged Micelles-Hydrogel for Targeted and Prolonged Drug Delivery in Chronic Wound Infections

Chronic diabetic wounds are a growing threat globally. Many aspects contribute to its deterioration, including bacterial infection, unbalanced microenvironment, dysfunction of cell repair, etc. In this work, we designed a multipronged micelles-hydrogel platform loaded with curcumin and rifampicin (CRMs-hydrogel) for bacteria-infected chronic wound treatment. The curcumin- and rifampicin-loaded micelles (CRMs) exhibited both MMP9-responsive and epidermal growth factor receptor (EGFR)-targeting abilities. On the one hand, drugs could be released from micelles due to responsive disassembly by MMP9, a matrix metalloproteinase overexpressed in a chronic wound environment; on the other hand, CRMs showed specific targeting to EGFR on epithelial cells and fibroblasts and therefore increased intracellular drug delivery. The thermosensitive CRMs-hydrogel could form strong adhesion with the wound area and served as a suitable matrix for sustained release of CRMs directly at the wound bed, with excellent intracellular and extracellular bacterial elimination efficiency and wound healing promotion capability. We found that a single dose of CRMs-hydrogel achieved 99% antibacterial rate at the MRSA-infected diabetic wound, which effectively reduced inflammatory response and promoted the neovascularization and re-epithelialization process, with nearly half reduction of the skin barrier regeneration period. Collectively, our thermosensitive, MMP9-responsive, and targeted micelles-hydrogel nanoplatform is promising for chronic wound treatment.

Angiotensin converting enzyme inhibitors from medicinal plants: a molecular docking and dynamic simulation approach

Angiotensin converting enzyme (ACE) is a key enzyme and mediator in the aetiology of high blood pressure (HBP) and hypertension. As one of the leading cause of untimely death worldwide, there is a lot of research and studies on the management and treatment of hypertension. The usage of medicinal plants in the management of hypertension as alternative to synthetic allopathic drugs is a common practice in folkloric and traditional medicine. Therefore, this study was aimed to investigate the ACE inhibitory activity of some medicinal plants which are commonly used in the treatment of HBP in southwestern part of Nigeria using extensive in-silico approach. Compounds identified in the plants through GC-MS technique, together with Lisinopril were docked against ACE protein. It was observed that only 40 of the compounds had binding affinity ≥ - 6.8 kcal/mol which was demonstrated by the standard drug (lisinopril). Interaction between the compounds and ACE was via conventional hydrogen, carbon hydrogen, alkyl, pi-alkyl, pi-carbon, and Van Der Wall bonds among others. Most of these compounds exhibited drug like properties, without violating majority of the physicochemical descriptors and Lipinski rule of 5. The ADMET evaluation revealed that only 2 compounds (cyclopentadecanone and oxacycloheptadecan-2-one) which were identified in Bacopa florinbunda plant were predicted non-toxic and thus were subjected to molecular dynamics and simulation with ACE. From the molecular dynamics and mechanics analysis, both cyclopentadecanone and oxacycloheptadecan-2-one showed high stability and inhibitory potentials when bound to ACE. Oxacycloheptadecan-2-one was more stable than lisinopril and cyclopentadecanone in the ligand-ACE complex; we therefore suggested its experimental and clinical validation as drug candidates for the treatment of hypertension.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-022-00135-z.

Linker-Dependent Folding Rationalizes PROTAC Cell Permeability

Proteolysis-targeting chimeras (PROTACs) must be cell permeable to reach their target proteins. This is challenging as the bivalent structure of PROTACs puts them in chemical space at, or beyond, the outer limits of oral druggable space. We used NMR spectroscopy and molecular dynamics (MD) simulations independently to gain insights into the origin of the differences in cell permeability displayed by three flexible cereblon PROTACs having closely related structures. Both methods revealed that the propensity of the PROTACs to adopt folded conformations with a low solvent-accessible 3D polar surface area in an apolar environment is correlated to high cell permeability. The chemical nature and the flexibility of the linker were essential for the PROTACs to populate folded conformations stabilized by intramolecular hydrogen bonds, π-π interactions, and van der Waals interactions. We conclude that MD simulations may be used for the prospective ranking of cell permeability in the design of cereblon PROTACs.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Determining small-molecule permeation through lipid membranes

The passive permeability of cell membranes is of key importance in biology, biomedical research and biotechnology as it determines the extent to which various molecules such as drugs, products of metabolism, and toxins can enter or leave the cell unaided by dedicated transport proteins. The quantification of passive solute permeation is possible with radio-isotope distribution experiments, spectroscopic measurements and molecular dynamics simulations. This protocol describes stopped-flow fluorimetry measurements performed on lipid vesicles and living yeast cells to estimate the osmotic permeability of water and solutes across (bio)membranes. Encapsulation of the fluorescent dye calcein into lipid vesicles allows monitoring of volume changes upon osmotic shifts of the medium via (de)quenching of the fluorophore, which we interpret using a well-defined physical model that takes the dynamics of the vesicles into account to calculate the permeability coefficients of solutes. We also present analogous procedures to probe weak acid and base permeability in vesicles and cells by using the read-out of encapsulated or expressed pH-sensitive probes. We describe the preparation of synthetic vesicles of varying lipid composition and determination of vesicle size distribution by dynamic light scattering. Data on membrane permeation are obtained using either conventional or stopped-flow kinetic fluorescence measurements on instruments available in most research institutes and are analyzed with a suite of user-friendly MATLAB scripts ( https://doi.org/10.5281/zenodo.6511116 ). Collectively, these procedures provide a comprehensive toolbox for determining membrane permeability coefficients in a variety of experimental systems, and typically take 2-3 d.

Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain-a Game Changing Parameter for CNS Drug Discovery and Development

PURPOSE

More than 15 years have passed since the first description of the unbound brain-to-plasma partition coefficient (Kp,uu,brain) by Prof. Margareta Hammarlund-Udenaes, which was enabled by advancements in experimental methodologies including cerebral microdialysis. Since then, growing knowledge and data continue to support the notion that the unbound (free) concentration of a drug at the site of action, such as the brain, is the driving force for pharmacological responses. Towards this end, Kp,uu,brain is the key parameter to obtain unbound brain concentrations from unbound plasma concentrations.

METHODS

To understand the importance and impact of the Kp,uu,brain concept in contemporary drug discovery and development, a survey has been conducted amongst major pharmaceutical companies based in Europe and the USA. Here, we present the results from this survey which consisted of 47 questions addressing: 1) Background information of the companies, 2) Implementation, 3) Application areas, 4) Methodology, 5) Impact and 6) Future perspectives.

RESULTS AND CONCLUSIONS

From the responses, it is clear that the majority of the companies (93%) has established a common understanding across disciplines of the concept and utility of Kp,uu,brain as compared to other parameters related to brain exposure. Adoption of the Kp,uu,brain concept has been mainly driven by individual scientists advocating its application in the various companies rather than by a top-down approach. Remarkably, 79% of all responders describe the portfolio impact of Kp,uu,brain implementation in their companies as 'game-changing'. Although most companies (74%) consider the current toolbox for Kp,uu,brain assessment and its validation satisfactory for drug discovery and early development, areas of improvement and future research to better understand human brain pharmacokinetics/pharmacodynamics translation have been identified.

In silico-in vitro estimation of lipophilicity and permeability association for succinimide derivatives using chromatographic anisotropic systems and parallel artificial membrane permeability assay

Passive permeability is one of the key features that determine absorbability and one of the most studied properties in the early phases of drug development. Newly synthesized succinimide derivatives from two different series (1-aryl-3-methylsuccinimides and 1-aryl-3-ethyl-3-methylsuccinimides) with high biological potential have been subjected to estimation of their passive permeability and their association with (a) experimentally obtained anisotropic lipophilicity, (b) in silico-calculated lipophilicity and (c) in silico-predicted permeability and absorbability. Non-cellular-based parallel artificial membrane permeability assay was applied for quantifying their passive permeation, expressed as logP_app . Passive permeation was governed by the lipophilicity of the analysed compounds, and anisotropic lipophilicity was related with statistically significant passive transcellular diffusion (r²  = 0.614, P < 0.001). Moreover, experimentally determined passive permeability, logP_app , was statistically significantly associated with both in silico-predicted absorption constant, k_a (r²  = 0.7886, P < 0.001), and human intestinal absorption (HIA) in percentage (r²  = 0.484, P < 0.001), respectively. However, there was no statistically significant relationship between experimentally obtained permeability on non-cellular-based model and in silico-predicted Caco-2 permeability based on the predictions conducted on two different software. Based on the obtained results, anisotropic systems are promising surrogates for determining lipophilicity, except for compounds with acidic functional groups that are completely ionized under (pH = 7.4).

A Chemical Strategy toward Novel Brain-Penetrant EZH2 Inhibitors

Aberrant gene-silencing through dysregulation of polycomb protein activity has emerged as an important oncogenic mechanism in cancer, implicating polycomb proteins as important therapeutic targets. Recently, an inhibitor targeting EZH2, the methyltransferase component of PRC2, received U.S. Food and Drug Administration approval following promising clinical responses in cancer patients. However, the current array of EZH2 inhibitors have poor brain penetrance, limiting their use in patients with central nervous system malignancies, a number of which have been shown to be sensitive to EZH2 inhibition. To address this need, we have identified a chemical strategy, based on computational modeling of pyridone-containing EZH2 inhibitor scaffolds, to minimize P-glycoprotein activity, and here we report the first brain-penetrant EZH2 inhibitor, TDI-6118 (compound 5). Additionally, in the course of our attempts to optimize this compound, we discovered TDI-11904 (compound 21), a novel, highly potent, and peripherally active EZH2 inhibitor based on a 7 member ring structure.

In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species

Accurate prediction of human clearance is of critical importance in drug discovery. In this study, in vitro - in vivo extrapolation (IVIVE) of hepatic clearance was established using large sets of compounds for four preclinical species (mouse, rat, dog, and non-human primate) to enable better understanding of clearance mechanisms and human translation. In vitro intrinsic clearances were obtained using pooled liver microsomes (LMs) or hepatocytes (HEPs) and scaled to hepatic clearance using the parallel-tube and well-stirred models. Subsequently, IVIVE scaling factors (SFs) were derived to best predict in vivo clearance. The SFs for extended clearance classification system (ECCS) class 2/4 compounds, involving metabolic clearance, were generally small (≤ 2.6) using both LMs and HEPs with parallel-tube model, with the exception of the rodents (~ 2.4-4.6), suggesting in vitro reagents represent in vivo reasonably well. SFs for ECCS class 1A and 1B are generally higher than class 2/4 across the species, likely due to the contribution of transporter-mediated clearance that is under-represented with in vitro reagents. The parallel-tube model offered lower variability in clearance predictions over the well-stirred model. For compounds that likely demonstrate passive permeability-limited clearance in vitro, rat LM predicted in vivo clearance more accurately than HEP. This comprehensive analysis demonstrated reliable IVIVE can be achieved using LMs and HEPs. Evaluation of clearance IVIVE in preclinical species helps to better understand clearance mechanisms, establish more reliable IVIVE in human, and enhance our confidence in human clearance and PK prediction, while considering species differences in drug metabolizing enzymes and transporters.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Evaluation of Drug Sorption on Laboratory Materials with Abraham Solvation Parameters of Drugs and its Prevention

PURPOSE

Undesired drug sorption on laboratory material surfaces reduces the performance of analytical methods and results in the generation of unreliable data. Hence, we characterized the sorption of drugs and evaluated the sorption extent using a linear free energy relationship (LFER) model with Abraham solvation parameters of drugs. Furthermore, to prevent sorption, the effects of additives, such as organic solvents and salts, were evaluated.

METHODS

The sorption of fifteen model drugs (concentration: 2 μM), with various physicochemical properties, on materials in 0.2% dimethyl sulfoxide aqueous solutions was evaluated. Drug sorption extent on the materials was determined using high-performance liquid chromatography. The obtained results were analyzed using an LFER model with Abraham solvation parameters of the drugs. The effect of additives on the sorption of itraconazole, one of the most hydrophobic drugs among those tested in this study, was investigated.

RESULTS

Sorption was dependent on the physicochemical properties of drugs, rather than the type of materials used, and additives altered the rate of drug sorption. Equations were developed to evaluate the sorption extent (nmol) of drugs to glass and polypropylene using the Abraham solvation parameters of the drugs.

CONCLUSIONS

LFER modeling with Abraham solvation parameters of drugs enabled us to evaluate drug sorption on materials. All the additives altered the rate of drug sorption, and some organic solvents effectively prevented sorption. The developed LFER model would be useful for assessment of the sorption properties of compounds in in vitro evaluations in drug discovery research and various other biochemical fields.

Structure-based design and synthesis of conformationally constrained derivatives of methyl-piperidinopyrazole (MPP) with estrogen receptor (ER) antagonist activity

Nuclear Estrogen receptors (ER) are cytoplasmic proteins; translocated to the nucleus to induce transcriptional signals after getting bound to the estrogen hormone. ER activation implicated in cancer cell proliferation of female reproductive organs. Thus, the discovery of ER antagonists is a reliable strategy to combat estrogen-dependent breast cancer. Endometrial carcinoma is one of the complications encountered upon long-term therapy by selective estrogen receptor modulators (SERMs) like Tamoxifen (TMX) and methyl piperidinopyrazole (MPP). Thus, the ER-full antagonist is a solution to improve the safety of this class of therapeutics during the treatment of breast cancer. We selected MPP as a lead structure to design conformationally constrained analogs. Structural rigidification is a proven strategy to transform the SERMs into full antagonists. Accordingly, we synthesized 7-methoxy-3-(4-methoxyphenyl)-4,5-dihydro-2H-benzo[g]indazoles (4), (6a-c),(8-12) along with the biphenolic counterparts(13-19)that are the anticipated active metabolites. The 4-nitrophenyl derivative(4)is with the most balanced profile regardingthe in vivoanti-uterotrophic potential (EC₅₀ = 4.160 μM); and the cytotoxicity assay of the corresponding active metabolite(13)against ER+ breast cancer cell lines (MCF-7 IC₅₀ = 7.200 μM, T-47D IC₅₀ = 11.710 μM). The inconsiderable uterotrophic activities of the elaborated ER-antagonists and weak antiproliferative activity of the compound(13)against ovarian cancer (SKOV-3 IC₅₀ = 29.800 μM) highlighted it as a good start point to elaborate potential ER-full antagonists devoid of endometrial carcinoma. Extending the pendant chain that protrudes from the 2-(4-(substituted)-phenyl) ring of the new benzo-indazoles is recommended for enhancing the potency based on the binding mode of compound(13)in the ligand-binding domain (LBD) of ER.

Route of intracellular uptake and cytotoxicity of sesamol, sesamin, and sesamolin in human melanoma SK-MEL-2 cells

The intracellular uptake concentration determines drug absorption, drug activity, and toxicity. Sesamol, sesamin, and sesamolin are promising bioactive components from Sesame indicum L. Their respective intracellular uptake pathway and cytotoxicity were evaluated using melanoma and non-cancerous cells. Quantitative structure-activity relationship (QSAR) models were built to identify the molecular features affecting drug uptake in cells. The respective intracellular uptake pathway for sesamol vs. sesamin and sesamolin was carrier-mediated vs. passive transport. Topological polar surface area (PSA) and 2D autocorrections increase the intracellular concentration (C/M ratio) of these compounds. Sesamol has the lowest C/M ratio compared to sesamin and sesamolin, but only sesamol inhibits the cell viability of melanoma and provides an inhibition concentration at 50% (IC₅₀) against melanoma cells. The slightly aqueous solubility of sesamin and sesamolin, therefore, limits testing of their cytotoxicity. In conclusion, sesamol has the potential to inhibit melanoma cell growth, but requires improvement of the C/M ratio to increase its physicochemical properties. Thus, in order to investigate the cytotoxicity of sesamin and sesamolin against melanoma cells a solubility enhancer is needed.

Structure-Based Discovery of Proline-Derived Arginase Inhibitors with Improved Oral Bioavailability for Immuno-Oncology

Recent data suggest that the inhibition of arginase (ARG) has therapeutic potential for the treatment of a number of indications ranging from pulmonary and vascular disease to cancer. Thus, high demand exists for selective small molecule ARG inhibitors with favorable druglike properties and good oral bioavailability. In light of the significant challenges associated with the unique physicochemical properties of previously disclosed ARG inhibitors, we use structure-based drug design combined with a focused optimization strategy to discover a class of boronic acids featuring a privileged proline scaffold with superior potency and oral bioavailability. These compounds, exemplified by inhibitors 4a, 18, and 27, demonstrated a favorable overall profile, and 4a was well tolerated following multiple days of dosing at concentrations that exceed those required for serum arginase inhibition and concomitant arginine elevation in a syngeneic mouse carcinoma model.

Cell Permeability of Isomeric Macrocycles: Predictions and NMR Studies

Conformation-dependent 3D descriptors have been shown to provide better predictions of the physicochemical properties of macrocycles than 2D descriptors. However, the computational identification of relevant conformations for macrocycles is nontrivial. Herein, we report that the Caco-2 cell permeability difference between a pair of diastereomeric macrocycles correlated with their solvent accessible 3D polar surface area and radius of gyration. The descriptors were calculated from the macrocycles’ solution-phase conformational ensembles and independently from ensembles obtained by conformational sampling. Calculation of the two descriptors for three other stereo- and regioisomeric macrocycles also allowed the correct ranking of their cell permeability. Methods for conformational sampling may thus allow ranking of passive permeability for moderately flexible macrocycles, thereby contributing to the prioritization of macrocycles for synthesis in lead optimization.

Organic Cation Transporter 1 an Intestinal Uptake Transporter: Fact or Fiction?

Intestinal transporter proteins are known to affect the pharmacokinetics and in turn the efficacy and safety of many orally administered drugs in a clinically relevant manner. This knowledge is especially well-established for intestinal ATP-binding cassette transporters such as P-gp and BCRP. In contrast to this, information about intestinal uptake carriers is much more limited although many hydrophilic or ionic drugs are not expected to undergo passive diffusion but probably require specific uptake transporters. A transporter which is controversially discussed with respect to its expression, localization and function in the human intestine is the organic cation transporter 1 (OCT1). This review article provides an up-to-date summary on the available data from expression analysis as well as functional studies in vitro, animal findings and clinical observations. The current evidence suggests that OCT1 is expressed in the human intestine in small amounts (on gene and protein levels), while its cellular localization in the apical or basolateral membrane of the enterocytes remains to be finally defined, but functional data point to a secretory function of the transporter at the basolateral membrane. Thus, OCT1 should not be considered as a classical uptake transporter in the intestine but rather as an intestinal elimination pathway for cationic compounds from the systemic circulation.