The Critical Role of Passive Permeability in Designing Successful Drugs

Human organotypic colon in vitro microtissue: unveiling a new window into colonic drug disposition

The purpose of this study was to evaluate EpiColon, a novel human organotypic 3D colon microtissue prototype, developed to assess colonic drug disposition, with a particular focus on permeability ranking, and compare its performance to Caco-2 monolayers. EpiColon was characterized for barrier function using transepithelial electrical resistance (TEER), morphology via histology and immunohistochemistry, and functionality through drug transport studies measuring apparent permeability (Papp). Cutoff thresholds for the permeability of FITC-dextran 4 kDa (FD4), FITC-dextran 10 kDa (FD10S), and [14C]mannitol were established to monitor microtissue integrity. Permeability of EpiColon for 20 benchmark drugs was compared with Caco-2 data, and the activity of pivotal efflux transporters, including multidrug resistance protein 1/P-glycoprotein (MDR1/P-gp), along with multidrug resistance protein 2 (MRP2) and breast cancer resistance protein (BCRP), was evaluated using selective substrates. EpiColon exhibited a physiological barrier function (272.0 ± 53.05 Ω x cm2) and effectively discriminated between high (e.g., budesonide and [3H]metoprolol) and low permeable compounds (e.g., [3H]atenolol and [14C]mannitol). The model demonstrated functional activity for key efflux transporters, with efflux ratios of 2.32 for [3H]digoxin (MDR1/P-gp) and 3.34 for sulfasalazine (MRP2 and BCRP). Notably, EpiColon showed an enhanced dynamic range in the low permeability range, differentiating Papp between FD4 and FD10S, in contrast to Caco-2 monolayers. Significant positive correlations were observed between human fraction absorbed (fabs) and logarithmically transformed Papp [AP-BL] values for both EpiColon (rs = 0.68) and Caco-2 (rs = 0.68). Furthermore, EpiColon recapitulates some essential phenotypic and cellular features of the human colon, including the expression of critical marker genes (Pan-Cytokeratin+: epithelial/colonocytes, Vimentin+: mesenchymal/fibroblast, and Alcian Blue+: goblet cell/mucus). In conclusion, EpiColon is a promising platform that offers a valuable complement to conventional Caco-2 monolayers for studying colonic drug disposition. However, the presence of flat and some cuboidal cells, along with low throughput, must be addressed to improve its applicability in both academic research and pharmaceutical industry.

Antioxidant and Neuroprotective Properties of Selected Pyrrole-Containing Azomethine Compounds in Neurotoxicity Models In Vitro

Neurodegenerative diseases involve oxidative stress and enzyme dysregulation, necessitating novel neuroprotective agents. This study evaluates the neuroprotective and antioxidant potential of seven pyrrole-based compounds with predicted radical scavenging activity and inhibitory effects on monoamine oxidase B (MAO-B) and acetylcholinesterase (AChE). The compounds were tested in vitro using SH-SY5Y neuroblastoma cells and subcellular rat brain fractions, including synaptosomes, mitochondria, and microsomes. Neuroprotective and antioxidant effects were assessed in oxidative stress models, including H2O2-induced stress in SH-SY5Y cells, 6-hydroxydopamine toxicity in synaptosomes, tert-butyl hydroperoxide-induced stress in mitochondria, and non-enzyme lipid peroxidation in microsomes. In silico screening for lipophilicity, hydrogen bonding, total polar surface area (TPSA), and ionization properties, was performed to evaluate bioavailability. All compounds exhibited a weak neurotoxic effect on the subcellular fractions at a concentration of 100 µM. However, in oxidative stress models, they demonstrated significant neuroprotective and antioxidant effects at 100 µM. In SH-SY5Y cells, compounds 7, 9, 12, 14, and 15 exhibited low toxicity and strong protective effects at concentrations as low as 1 µM. In silico analysis prioritized compounds 1, 7, 9, 12, and 14 for further development based on their favorable bioavailability. The tested pyrrole-based compounds exhibit promising neuroprotective and antioxidant properties, with several candidates showing potential for further development based on both in vitro efficacy and predicted oral bioavailability.

Mechanistic studies on pH-permeability relationships: Impact of the membrane polar headgroup region on pKa

Passive permeability through biological membranes requires partitioning of drug molecules into the lipid bilayer and subsequent permeation. Most drugs are weak acids or bases, making their ionization constants (pKa) critical for predicting permeation across biological barriers. The pH-partition hypothesis posits that only the uncharged form contributes to passive permeability, suggesting a proportional relationship between permeability and uncharged fraction. However, experimental pH-permeability profiles are not accurately predicted with neutral fractions calculated using aqueous pKa values. Interactions between charged solutes and phospholipids are expected to alter the pKa of drugs within the membrane. In this study, we use modeling and simulation and experimental partitioning in a biphasic surrogate phospholipid membrane system, diacetyl phosphatidylcholine (DAcPC) and n-hexane, to study pH dependent permeability. Models were constructed in which pKa values were either shifted or distributed around the aqueous pKa and the resulting neutral fractions were compared to pH-dependent permeabilities. For acids, models with shifted or distributed pKa values can explain pH-dependent permeabilities in Caco-2 cells, but these models were not predictive for bases. For partitioning studies, five probe drugs, two acidic (ketoprofen, tolbutamide), two basic (metoprolol, verapamil), and one neutral (diazepam), were partitioned between n-hexane and buffer or buffer-hydrated DAcPC at different pH values. The apparent pKa values in the surrogate phospholipid system (C6/DAcPC) were shifted from their aqueous pKa values. However, the resulting pKa values did not predict observed pH-dependent Caco-2 permeabilities. Models that decrease the pH-pKa difference improve permeability predictions for both bases and acids and use of a pKa shift or distribution can further improve predictions for acids.

Is Cocaine Protonated When it Binds to the Dopamine Transporter?

There has been much controversy about whether the well-known alkaloid and tertiary amine base cocaine (pK a = 8.5) binds to the human dopamine transporter (DAT) in its protonated form. Most potent DAT inhibitors are also strong amines-yet there are some noteworthy examples where neutral cocaine analogues have high affinity, while the quaternary ammonium analog of cocaine, cocaine methiodide, is a comparatively poor inhibitor. In this paper, we show that a fluorescent cocaine analog, with a lower pK a than cocaine, becomes protonated in the DAT binding site and conclude that similar behavior must be expected from cocaine. By determining the pK a of the aspartate residue in DAT believed to interact with the amine of cocaine, we are able to explain the apparently contradictory structure-activity data of cocaine analogues.

Exploring the anti-diabetic potential of barnyard millet: insights from virtual screening, MD simulation and MM-PBSA

Barnyard millet (Echinochloa frumentacea) is a nutritionally superior grain and a rich source of dietary fiber, and protein. It helps in managing health and dietary issues such as malnutrition, diabetes, obesity, and celiac disease. Its low content of slowly digestible carbohydrates promotes a gradual release of glucose, helping to maintain stable blood glucose levels. The present study aims to identify and screen phytochemicals in the barnyard millet and explore its anti-diabetic activity through an in-silico study. Gas chromatography-mass spectrometry (GC-MS) analyses of the seed extract revealed the occurrence of 73 bioactive compounds that are known to possess a variety of pharmacological activities. Based on the virtual screening analysis, phytochemicals interacted with five different diabetic targets, with diosgenin demonstrating the lowest binding affinity across four receptors. Specifically, diosgenin showed a binding affinity of -9.2 kcal/mol with the Insulin receptor (PDB ID: 1IR3), -8.7 kcal/mol with Peroxisome proliferator-activated receptors (PDB ID: 3G9E), -7.5 kcal/mol with Tyrosine phosphatase 1-beta (2F70), and -6.5 kcal/mol with the Glucagon receptor (PDB ID: 5EE7). For Aldose reductase (PDB ID: 4XZH), Docosahexaenoic acid exhibited the lowest binding affinity of -9.9 kcal/mol. The dynamic behavior of 2F70-Diosgenin docked complexes throughout a 500 ns trajectory run was investigated further. The RMSD and RMSF analyses reveal that the complex remains structurally stable. The binding free energies were computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodology. The calculation results show that the predicted free energies of the complex are stable. These results suggest that the 2F70-Diosgenin complex is stable, highlighting its potential for further wet lab validation.

Meta-Analysis of Permeability Literature Data Shows Possibilities and Limitations of Popular Methods

Permeability is an important molecular property in drug discovery, as it co-determines pharmacokinetics whenever a drug crosses the phospholipid bilayer, e.g., into the cell, in the gastrointestinal tract, or across the blood-brain barrier. Many methods for the determination of permeability have been developed, including cell line assays (CACO-2 and MDCK), cell-free model systems like parallel artificial membrane permeability assay (PAMPA) mimicking, e.g., gastrointestinal epithelia or the skin, as well as the black lipid membrane (BLM) and submicrometer liposomes. Furthermore, many in silico approaches have been developed for permeability prediction: meta-analysis of publicly available databases for permeability data (MolMeDB and ChEMBL) was performed to establish their usability. Four experimental and two computational methods were evaluated. It was shown that repeatability of the reported permeability measurement is not great even for the same method. For the PAMPA method, two different permeabilities are reported: intrinsic and apparent. They can vary in degrees of magnitude; thus, we suggest being extra cautious using literature data on permeability. When we compared data for the same molecules using different methods, the best agreement was between cell-based methods and between BLM and computational methods. Existence of unstirred water layer (UWL) permeability limits the data agreement between cell-based methods (and apparent PAMPA) with data that are not limited by UWL permeability (computational methods, BLM, intrinsic PAMPA). Therefore, different methods have different limitations. Cell-based methods provide results only in a small range of permeabilities (-8 to -4 in cm/s), and computational methods can predict a wider range of permeabilities beyond physical limitations, but their precision is therefore limited. BLM with liposomes can be used for both fast and slow permeating molecules, but its usage is more complicated than standard transwell techniques. To sum up, when working with in-house measured or published permeability data, we recommend caution in interpreting and combining them.

Property-Based Design of Xanthine Derivatives as Potent and Orally Available TRPC4/5 Inhibitors for Depression and Anxiety

Transient receptor potential canonical channels 4 and 5 (TRPC4/5) are nonselective cation channels involved in emotional regulation, positioning them to be promising targets for treating mental disorders such as anxiety and depression. HC-070, a potent TRPC4/5 inhibitor, exhibits significant anxiolytic and antidepressant effects in animal models, though its drug-like properties require optimization. In this study, we applied a property-based drug design (PBDD) approach to optimize HC-070, leading to the discovery of compound 32, which shows improved LipE and Fsp3 values, reduced hERG blocking activity, enhanced metabolic stability, increased aqueous solubility, and superior oral bioavailability. Oral administration of compound 32 in mouse models demonstrates anxiolytic and antidepressant efficacy comparable to fluoxetine. This study supports the therapeutic potential of TRPC4/5 inhibitors for mental disorders and identifies compound 32 as a promising candidate for further investigation. Furthermore, our work underscores the value of PBDD in optimizing lead compounds during drug discovery process.

A comprehensive insight of innovations and recent advancements in nanocarriers for nose-to-brain drug targeting

Central Nervous System (CNS) disorders are the leading cause of illness and affect the everyday lives of people all around the globe and are predicted to increase tremendously in the upcoming decades. Traditional methods of delivering drugs to the CNS face considerable limitations. Nose-to-brain targeting offers a promising alternative that bypasses the blood-brain barrier (BBB), enabling targeted drug administration to the central nervous system (CNS). Nanotechnology has brought forward innovative solutions to the challenges of drug delivery in CNS disorders. Nanocarriers such as liposomes, nanoparticles, nanoemulsions and dendrimers can enhance drug stability, bioavailability, and targeted delivery to the brain. These nanocarriers are designed to overcome physiological barriers and provide controlled and sustained drug release directly to the CNS. Nanocarrier technology has made significant strides in recent years, enabling more effective and targeted delivery of drugs to the brain. With recent advancements, intranasal delivery coupled with nanocarriers seems to be a promising combination that can provide better clinical profiles, pharmacokinetics, and pharmacodynamics for neurodegenerative disorders. This study focuses on exploring the nose-to-brain drug delivery system, emphasizing the use of various nanocarriers designed for this purpose. Additionally, the study encompasses recent advancements in nanocarrier technology tailored specifically to improve the efficiency of drug administration through the nasal route to the brain.

Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties

Drug discovery's success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and excretion of drug molecules, thereby determining the pharmacokinetic and toxicokinetic properties important for drug development. Intrinsic permeability (P0) is more crucial than apparent permeability (Papp) in assessing the transport of drug molecules across a membrane. It gives more consistent results due to its non-dependency on external/site-specific factors. In the present work, our focus is on the construction of a machine learning (ML)-based quantitative read-across structure-property relationship (q-RASPR) model of intrinsic permeability of drug molecules by utilizing both linear and non-linear algorithms. The Support Vector Regression (SVR) q-RASPR model was found to be the best model having superior predictive ability (Q2F1 = 0.788, Q2F2 = 0.785, MAEtest = 0.637). The contribution of important descriptors in the final model is explained to get a mechanistic interpretation of intrinsic permeability. Overall, the present study unveils the application of the q-RASPR framework for significant improvement of the external predictivity of the traditional QSPR model in the case of intrinsic permeability to get a better assessment of the total permeability of drug molecules.

Rhanterium Epapposum Essential Oil and Its Primary Compounds Control Infection, Inflammation, and Serum Electrolyte Imbalance in Mice with Giardiasis

PURPOSE

The present experimental study seeks to evaluate the in vitro and in vivo effects, as well as the potential mechanisms of action, of Rhanterium epapposum essential oil (REE) and its main constituents against Giardia lamblia infection.

METHODS

The analysis of REE was performed using the Gas Chromatography-Mass Spectrometry (GC-MS) detector. The in vitro effects of REE and its main constituents on viability of G. lamblia cysts and the human intestinal epithelial cell line, as well as their effects on plasma membrane permeability, the reactive oxygen species (ROS) generation, and trophozoite adherence were assessed by MTT assay. Effects of oral administration of REE and its main constituents at doses 10 and 20 mg/kg on the number of Giardia cysts, the serum level of electrolytes of sodium (Na+) and potassium (K+), as well as the gene expression of inflammatory genes of TNF-α, IL-1β, IL-10, NF-κB p65, and Toll-like receptor 4 (TLR4)) were assessed.

RESULTS

The 50% inhibitory concentrations (IC50) value of REE, MC, CP, LN and MTZ against G. lamblia cysts was 26.7, 32.1, 35.3, 45.1, and 42.1 µg/mL, respectively; indicated that REE, MC, and CP displayed high antigiardial effects in comparison with MTZ. The obtained 50% cytotoxic concentration (CC50) value of REE, MC, CP, LN, and MTZ on HIEC-6 cells was 279.1, 363.4, 394.2, 478.3, and 422.1 µg/mL, respectively. REE and its main compounds significantly (p < 0.001) increased the plasma membrane permeability and ROS generation in Giardia trophozoites, while, reduced the Giardia trophozoites adherence. In vivo, REE, MC, CP, and LN mainly at the dose of 20 mg/kg considerably reduced the number and viability of Giardia cysts, modulated the serum level of electrolytes Na and K in the infected mice compared with the mice treated with MTZ (P < 0.01); whereas inhibited the inflammation cytokines in the infected mice compared with the mice treated with MTZ (p < 0.001).

CONCLUSION

The current investigation showed that R. epapposum essential oil and its main compounds controlled the Giardia infection in mice by inhibiting inflammation, and serum electrolyte imbalance. After confirmation in further studies, these compounds may be considered for development as a novel therapeutic option for the treatment of giardiasis.

A membrane permeability database for nonpeptidic macrocycles

The process of developing new drugs is arduous and costly, particularly for targets classified as "difficult-to-drug." Macrocycles show a particular ability to modulate difficult-to-drug targets, including protein-protein interactions, while still allowing oral administration. However, the determination of membrane permeability, critical for reaching intracellular targets and for oral bioavailability, is laborious and expensive. In silico methods are a cost-effective alternative, enabling predictions prior to compound synthesis. Here, we present a comprehensive online database ( https://swemacrocycledb.com/ ), housing 5638 membrane permeability datapoints for 4216 nonpeptidic macrocycles, curated from the literature, patents, and bioactivity repositories. In addition, we present a new descriptor, the "amide ratio" (AR), that quantifies the peptidic nature of macrocyclic compounds, enabling the classification of peptidic, semipeptidic, and nonpeptidic macrocycles. Overall, this resource fills a gap among existing databases, offering valuable insights into the membrane permeability of nonpeptidic and semipeptidic macrocycles, and facilitating predictions for drug discovery projects.

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.

Exploring the Potential of Nasal Drug Delivery for Brain Targeted Therapy: A Detailed Analysis

The brain is a sensitive organ with numerous essential functions and complex mechanisms. It is secluded and safeguarded from the external environment as part of the central nervous system (CNS), serving as a sanctuary. By regulating their selective and specific absorption, efflux, and metabolism in the brain, the CNS controls brain homeostasis and the transit of endogenous and foreign substances. The mechanism which protects the brain from environmental chemicals, also prevent the entry of therapeutic chemicals to it. The delivery of molecules to the brain is hindered by several major barriers, such as the blood-brain barrier (BBB), blood-cerebrospinal fluid barrier (BCSFB), and blood-tumor barrier. BBB is formed by the combination of cerebral endothelial cells, astrocytes, neurons, pericytes and microglia. It is a tight junction of capillary endothelial cells, preventing the diffusion of solute into the brain. BCSFB is the second barrier, located at the choroid plexus, separating the blood from cerebrospinal fluid (CSF). It is comparatively more permeable than BBB. An uneven distribution of microvasculature across the tumor interstitial compromises drug delivery to neoplastic cells of a solid tumor, resulting in spatially inconsistent drug administration. Nasal drug delivery to the brain is a method of drug delivery that tries to deliver therapeutic substances directly from the nasal cavity to the central nervous system including the brain. In this review, besides the role of barriers we have discussed in detail about approaches adapted to deliver drugs to the brain along with mechanisms through nasal route. Further, different commercial formulations, clinical trials and patents have been thoroughly elaborated to date. The findings suggest that the nose-to-brain drug delivery method holds promise as an evolving approach, potentially contributing to the specific and targeted delivery of drugs into the brain.

Aniline Derivatives Containing 1-Substituted 1,2,3-Triazole System as Potential Drug Candidates: Pharmacokinetic Profile Prediction, Lipophilicity Analysis Using Experimental and In Silico Studies

Background: The triazole ring is an attractive structural unit in medicinal chemistry, and chemical compounds containing this type of system in their structure exhibit a wide spectrum of biological activity. They are used in the development of new pharmaceuticals. One of the basic parameters considered in the initial phase of designing potential drugs is lipophilicity, which affects the bioavailability and pharmacokinetics of drugs. Methods: The study aimed to assess the lipophilicity of fifteen new triazole derivatives of aniline using reversed phase thin layer chromatography (RP-TLC) and free web servers. Based on in silico methods, the drug similarity and pharmacokinetic profile (ADMET) of synthesized molecules were assessed. Results: A relationship was observed between the structure of the title compound, including the position of substitution in the aniline ring, and the experimental values of lipophilicity parameters (logPTLC). Most of the algorithms used to determine theoretical logP values showed less sensitivity to structural differences of the tested molecules. All obtained derivatives satisfy the drug similarity rules formulated by Lipinski, Ghose and Veber. Moreover, in silico analysis of the ADME profile showed favorable values of parameters related to absorption.

Investigating experimental vs. Predicted pKa values for PET radiotracer

The prediction of central nervous system (CNS) active pharmaceuticals and radiopharmaceuticals has experienced a boost by the introduction of computational approaches, like blood-brain barrier (BBB) score or CNS multiparameter optimization values. These rely heavily on calculated pKa values and other physicochemical parameters. Despite the inclusion of various physicochemical parameters in online data banks, pKa values are often missing and published experimental pKa values are limited especially for radiopharmaceuticals. This comparative study investigated the discrepancies between predicted and experimental pKa values and their impact on CNS activity prediction scores. The pKa values of 46 substances, including therapeutic drugs and PET imaging radiopharmaceuticals, were measured by means of potentiometry and spectrophotometry. Experimentally obtained pKa values were compared with in silico predictions (Chemicalize/Marvin). The results demonstrate a considerable discrepancy between experimental and in silico values, with linear regression analysis showing intermediate correlation (R2(Marvin) = 0.88, R2(Chemicalize) = 0.82). This indicates that if one requires an accurate pKa value, it is essential to experimentally assess it. This underscores the importance of experimentally determining pKa values for accurate drug design and optimization. The study's data provide a valuable library of reliable experimental pKa values for therapeutic drugs and radiopharmaceuticals, aiding researchers in the field.

Effect of molecular structure on membrane diffusion: Triphenylmethanes across Escherichia coli studied by second harmonic light scattering

Understanding how the structure of molecules affects their permeability across cell membranes is crucial for many topics in biomedical research, including the development of drugs. In this work, we examine the transport rates of structurally similar triphenylmethane dyes, malachite green (MG) and brilliant green (BG), across the membranes of living Escherichia coli (E. coli) cells and biomimetic liposomes. Using the time-resolved second harmonic light scattering technique, we found that BG passively diffuses across the E. coli cytoplasmic membrane (CM) 3.8 times faster than MG. In addition, BG exhibits a diffusion rate 3.1 times higher than MG across the membranes of liposomes made from E. coli polar lipid extracts. Measurements on these two molecules, alongside previously studied crystal violet (CV), another triphenylmethane molecule, are compared against the set of propensity rules developed by Lipinski and co-workers for assessing the permeability of hydrophobic ion-like drug molecules through biomembranes. It indicates that BG's increased diffusion rate is due to its higher lipophilicity, with a distribution coefficient 25 times greater than MG. In contrast, CV, despite having similar lipophilicity to MG, shows negligible permeation through the E. coli CM on the observation scale, attributed to its more hydrogen bonding sites and larger polar surface area. Importantly, cell viability tests revealed that BG's antimicrobial efficacy is ∼2.4 times greater than that of MG, which aligns well with its enhanced diffusion into the E. coli cytosol. These findings offer valuable insights for drug design and development, especially for improving the permeability of poorly permeable drug molecules.

Heteroaryl derivatives of suvorexant as OX1R selective PET ligand candidates: Cu-mediated 18F-fluorination of boroxines, in vitro and initial in vivo evaluation

BACKGROUND

The orexin receptor (OXR) plays a role in drug addiction and is aberrantly expressed in colorectal tumors. Subtype-selective OXR PET ligands suitable for in vivo use have not yet been reported. This work reports the development of 18F-labeled OXR PET ligand candidates derived from the OXR antagonist suvorexant and the OX1R-selective antagonist JH112.

RESULTS

Computational analysis predicted that fluorine substitution (1e) and introduction of the fluorobenzothiazole scaffold (1f) would be suitable for maintaining high OX1R affinity. After multi-step synthesis of 1a-1f, in vitro OXR binding studies confirmed the molecular dynamics calculations and revealed single-digit nanomolar OX1R affinities for 1a-f, ranging from 0.69 to 2.5 nM. The benzothiazole 1f showed high OX1R affinity (Ki = 0.69 nM), along with 77-fold subtype selectivity over OX2R. Cu-mediated 18F-fluorination of boroxine precursors allowed for a shortened reaction time of 5 min to provide the non-selective OXR ligand [18F]1c and its selective OX1R congener [18F]1f in activity yields of 14% and 22%, respectively, within a total synthesis time of 52-76 min. [18F]1c and [18F]1f were stable in plasma and serum in vitro, with logD7.4 of 2.28 ([18F]1c) and 2.37 ([18F]1f), and high plasma protein binding of 66% and 77%, respectively. Dynamic PET imaging in rats showed similar brain uptake of [18F]1c (0.17%ID/g) and [18F]1f (0.15%ID/g). However, preinjection of suvorexant did not significantly block [18F]1c or [18F]1f uptake in the rat brain. Pretreatment with cyclosporine A to study the role of P-glycoprotein (P-gp) in limiting brain accumulation moderately increased brain uptake of [18F]1c and [18F]1f. Accordingly, in vitro experiments demonstrated that the P-gp inhibitor zosuquidar only moderately inhibited polarized, basal to apical transport of 1c (p < 0.05) and had no effect on the transport of 1f, indicating that P-gp does not play a relevant role in brain accumulation of [18F]1c and [18F]1f in vivo.

CONCLUSIONS

The in vitro and in vivo results of [18F]1c and [18F]1f provide a solid basis for further development of suitable OXR PET ligands for brain imaging.

High-Throughput SFC-MS/MS Method to Measure EPSA and Predict Human Permeability

Permeability is a key factor driving the absorption of orally administered drugs. In early discovery, the efficient evaluation of permeability, particularly for compounds violating Lipinski's Rule of 5, remains challenging. Addressing this, we established a high-throughput method to measure the experimental polar surface area (HT-EPSA) as an in vitro surrogate to measure permeability. Compared to earlier methods, HT-EPSA significantly reduces data acquisition time with enhanced sensitivity, selectivity, and data quality. In the effort of translating EPSA to human in vitro and in vivo passive permeability, we demonstrated the application of EPSA for predicting Caco-2 cell and human intestinal permeability, showing improvements over topological polar surface area and the parallel artificial membrane permeability assay for rank-ordering permeability in a proteolysis targeting chimera case study. The HT-EPSA method is expected to be highly beneficial in guiding early stage compound rank-ordering, faster decision-making, and in predicting in vitro and/or in vivo human intestinal permeability.

Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report

This Article shares the proceedings from the August 29th, 2023 (day 1) workshop "Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives". The focus of the day was on model parametrization; regulatory authorities from Canada, the USA, Sweden, Belgium, and Norway presented their views on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment processes, content, and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parametrizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs amorphous drugs; impact of excipients on apparent drug solubility/supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to predict the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations/species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract.

Beyond cytotoxic potency: disposition features required to design ADC payload

1. Antibody-drug conjugates (ADCs) have demonstrated impressive clinical usefulness in treating several types of cancer, with the notion of widening of the therapeutic index of the cytotoxic payload through the minimisation of the systemic toxicity. Therefore, choosing the most appropriate payload molecule is a particularly important part of the early design phase of ADC development, especially given the highly competitive environment ADCs find themselves in today.2. The focus of the current review is to describe critical attributes/considerations needed in the discovery and ultimately development of cytotoxic payloads in support of ADC design. In addition to potency, several key dispositional characteristics including solubility, permeability and bystander effect, pharmacokinetics, metabolism, and drug-drug interactions, are described as being an integral part of the integrated activities required in the design of clinically safe and useful ADC therapeutic agents.

Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers

The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Caco-2 monolayers are commonly employed to assess permeability across the intestinal epithelium, with an extensive number of apparent permeability coefficient (Papp) values available in the literature and a significant fraction collected in databases. The compilation of these Papp values for large datasets allows for the application of artificial intelligence tools for establishing quantitative structure-permeability relationships (QSPRs) to predict the permeability of new compounds from their structural properties. One of the main challenges that hinders the development of accurate predictions is the existence of multiple Papp values for the same compound, mostly caused by differences in the experimental protocols employed. This review addresses the magnitude of the variability within and between laboratories to interpret its impact on QSPR modelling, systematically and quantitatively assessing the most common sources of variability. This review emphasizes the importance of compiling consistent Papp data and suggests strategies that may be used to obtain such data, contributing to the establishment of robust QSPRs with enhanced predictive power.

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/.

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.

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.

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.

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.

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.

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 Nα -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, Nα -methylation, can enhance the performance of cell-penetrating peptides.

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.

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.

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.