Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein

Utilizing surface plasmon resonance as a novel method for monitoring in-vitro P-glycoprotein efflux

P-glycoprotein (Pgp) is known for its dichotomous roles as both a safeguarding efflux transporter against xenobiotics and as a catalyst for multidrug resistance. Given the susceptibility of numerous therapeutic compounds to Pgp-mediated resistance, compliance with Food and Drug Administration (FDA) guidelines mandates an in-depth in vitro transport assay during drug development. This study introduces an innovative transport assay that aligns with these regulatory imperatives but also addresses limitations in the currently established techniques. Using Pgp-reconstituted liposomes and employing surface plasmon resonance (SPR), this study developed a distinct method of measuring the relative transport rates of Pgp substrates in a controlled microenvironment. The Pgp substrates selected for this study-quinidine, methadone, and desipramine-resulted in transport ratios that corroborate with trends previously observed. To assess the kinetics of Pgp-mediated transport, the results were analyzed by fitting the data to both currently proposed Pgp substrate translocation models-the vacuum cleaner and flippase models. While the resulting kinetic analysis in this study lends support predominantly to the vacuum cleaner model, this study most notably developed a novel method of assessing Pgp-mediated transport rates and real-time kinetics using surface plasmon resonance.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

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.

Discovery of the Next Generation of Non-peptidomimetic Neurolysin Activators with High Blood-Brain Barrier Permeability: a Pharmacokinetics Study in Healthy and Stroke Animals

PURPOSE

There is growing interest in seeking pharmacological activation of neurolysin (Nln) for stroke treatment. Discovery of central nervous system drugs remains challenging due to the protection of the blood-brain barrier (BBB). The previously reported peptidomimetic Nln activators display unsatisfactory BBB penetration. Herein, we investigate the next generation of non-peptidomimetic Nln activators with high BBB permeability.

METHODS

A BBB-mimicking model was used to evaluate their in vitro BBB permeability. Protein binding, metabolic stability, and efflux assays were performed to determine their unbound fraction, half-lives in plasma and brains, and dependence of BBB transporter P-glycoprotein (P-gp). The in vivo pharmacokinetic profiles were elucidated in healthy and stroke mice.

RESULTS

Compounds KS52 and KS73 out of this generation exhibit improved peptidase activity and BBB permeability compared to the endogenous activator and previous peptidomimetic activators. They show reasonable plasma and brain protein binding, improved metabolic stability, and independence of P-gp-mediated efflux. In healthy animals, they rapidly distribute into brains and reach peak levels of 18.69% and 12.10% injected dose (ID)/ml at 10 min. After 4 h, their total brain concentrations remain 7.78 and 12.34 times higher than their A50(minimal concentration required for enhancing 50% peptidase activity). Moreover, the ipsilateral hemispheres of stroke animals show comparable uptake to the corresponding contralateral hemispheres and healthy brains.

CONCLUSIONS

This study provides essential details about the pharmacokinetic properties of a new generation of potent non-peptidomimetic Nln activators with high BBB permeability and warrants the future development of these agents as potential neuroprotective pharmaceutics for stroke treatment.

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening

Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.

P-glycoprotein mechanical functional analysis using in silico molecular modeling: Pharmacokinetic variability according to ABCB1 c.2677G > T/A genetic polymorphisms

P-glycoprotein (P-gp) is a widely membrane-expressed multi-drug transporter. It is unclear whether the pharmacokinetic diversity of P-gp substrates is highly dependent on ABCB1 polymorphisms encoding P-gp. The purpose of this study is to analyze the mechanistic function of P-gp through in silico molecular modeling and to approach the resolution of controversy over pharmacokinetic differences according to ABCB1 polymorphisms. P-gp conformations of apo, ligand-docked, and outward-facing states can be modeled based on structural information of human P-gp. And polymorphic P-gp structures were constructed through homology modeling. ABCB1 c.2677G > T/A (Ala893Ser/Thr), did not correspond to P-gp's nucleotide-binding-domain (NBD) or drug-binding-pocket (DBP) or involve mechanical conformational changes. Although amino acid substitution by ABCB1 c.2677G > T/A caused a 30 % increased strain in an α-helix hinge between the NBD and DBP in P-gp's internal tunnel, there were no overall structural changes compared to wild-type. ABCB1 c.2677G > T/A may increase torsional energy, impacting conformational change rate, but this does not significantly affect P-gp's general functioning. Fexofenadine docking into P-gp's DBP explained the substrate interaction, but no effect by ABCB1 c.2677G > T/A was confirmed. Our findings provide additional insights useful in resolving the debate about the influence of ABCB1 polymorphisms on the interindividual pharmacokinetic variability of P-gp substrates.

Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters

Multidrug resistance (MDR1) and breast cancer resistance protein (BCRP) play important roles in drug absorption and distribution. Computational prediction of substrates for both transporters can help reduce time in drug discovery. This study aimed to predict the efflux activity of MDR1 and BCRP using multiple machine learning approaches with molecular descriptors and graph convolutional networks (GCNs). In vitro efflux activity was determined using MDR1- and BCRP-expressing cells. Predictive performance was assessed using an in-house dataset with a chronological split and an external dataset. CatBoost and support vector regression showed the best predictive performance for MDR1 and BCRP efflux activities, respectively, of the 25 descriptor-based machine learning methods based on the coefficient of determination (R2). The single-task GCN showed a slightly lower performance than descriptor-based prediction in the in-house dataset. In both approaches, the percentage of compounds predicted within twofold of the observed values in the external dataset was lower than that in the in-house dataset. Multi-task GCN did not show any improvements, whereas multimodal GCN increased the predictive performance of BCRP efflux activity compared with single-task GCN. Furthermore, the ensemble approach of descriptor-based machine learning and GCN achieved the highest predictive performance with R2 values of 0.706 and 0.587 in MDR1 and BCRP, respectively, in time-split test sets. This result suggests that two different approaches to represent molecular structures complement each other in terms of molecular characteristics. Our study demonstrated that predictive models using advanced machine learning approaches are beneficial for identifying potential substrate liability of both MDR1 and BCRP.

In silico approach identified benzoylguanidines as SARS-CoV-2 main protease (Mpro) potential inhibitors

The coronavirus disease-2019 (COVID-19) pandemic, caused by the novel coronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), became the highest public health crisis nowadays. Although the use of approved vaccines for emergency immunization and the reuse of FDA-approved drugs remains at the forefront, the search for new, more selective, and potent drug candidates from synthetic compounds is also a viable alternative to combat this viral disease. In this context, the present study employed a computational virtual screening approach based on molecular docking and molecular dynamics (MD) simulation to identify possible inhibitors for SARS-CoV-2 Mpro (main protease), an important molecular target required for the maturation of the various polyproteins involved in viral replication. The virtual screening approach selected four potential inhibitors against SARS-CoV-2 Mpro. In addition, MD simulation studies revealed changes in the positions of the ligands during the simulations compared to the complex obtained in the molecular docking studies, showing the benzoylguanidines LMed-110 and LMed-136 have a higher affinity for the active site compared to the other structures that tended to leave the active site. Besides, there was a better understanding of the formation and stability of the existing H-bonds in the formed complexes and the energetic contributions to the stability of the target-ligand molecular complexes. Finally, the in silico prediction of the ADME profile suggested that LMed-136 has drug-like characteristics and good pharmacokinetic properties. Therefore, from the present study, it can be suggested that these structures can inhibit SARS-CoV-2 Mpro. Nevertheless, further studies are needed in vitro assays to investigate the antiviral properties of these structures against SARS-CoV-2.

Permeability of Metformin across an In Vitro Blood-Brain Barrier Model during Normoxia and Oxygen-Glucose Deprivation Conditions: Role of Organic Cation Transporters (Octs)

Our lab previously established that metformin, a first-line type two diabetes treatment, activates the Nrf2 pathway and improves post-stroke recovery. Metformin's brain permeability value and potential interaction with blood-brain barrier (BBB) uptake and efflux transporters are currently unknown. Metformin has been shown to be a substrate of organic cationic transporters (Octs) in the liver and kidneys. Brain endothelial cells at the BBB have been shown to express Octs; thus, we hypothesize that metformin uses Octs for its transport across the BBB. We used a co-culture model of brain endothelial cells and primary astrocytes as an in vitro BBB model to conduct permeability studies during normoxia and hypoxia using oxygen-glucose deprivation (OGD) conditions. Metformin was quantified using a highly sensitive LC-MS/MS method. We further checked Octs protein expression using Western blot analysis. Lastly, we completed a plasma glycoprotein (P-GP) efflux assay. Our results showed that metformin is a highly permeable molecule, uses Oct1 for its transport, and does not interact with P-GP. During OGD, we found alterations in Oct1 expression and increased permeability for metformin. Additionally, we showed that selective transport is a key determinant of metformin's permeability during OGD, thus, providing a novel target for improving ischemic drug delivery.

Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development

Drug-induced liver injury (DILI) is a major factor influencing new drug withdrawal; therefore, an appropriate toxicity assessment at the preclinical stage is required. Previous in silico models have been established using compound information listed in large data sources, thereby limiting the DILI risk prediction for new drugs. Herein, we first constructed a model to predict DILI risk based on a molecular initiating event (MIE) predicted by quantitative structure-activity relationships, admetSAR parameters (e.g. cytochrome P450 reactivity, plasma protein binding, and water-solubility), and clinical information (maximum daily dose [MDD] and reactive metabolite [RM]) for 186 compounds. The accuracy of the models using MIE, MDD, RM, and admetSAR alone were 43.2%, 47.3%, 77.0%, and 68.9%, while the "predicted MIE + admetSAR + MDD + RM" model's accuracy was 75.7%. The contribution of MIE to the overall prediction accuracy was little effect or rather worsening it. However, it was considered that MIE was a valuable parameter and that it contributed to detect high DILI risk compounds in the early development stage. We next examined the effect of stepwise changes in MDD on altering the DILI risk and estimating the maximum safety dose (MSD) for clinical use based on structural information, admetSAR, and MIE parameters because it is important to estimate the dose that could prevent the DILI onset in clinical conditions. Low-MSD compounds might increase the DILI risk, as these compounds were classified as "most-DILI concern" at low doses. In conclusion, MIE parameters were especially useful to check the DILI concern compounds and to prevent the underestimation of DILI risk in the early stage of drug development.

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods

ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

Strategies for targeting the P2Y12 receptor in the central nervous system

The purinergic 2Y type 12 receptor (P2Y₁₂R) is a well-known biological target for anti-thrombotic drugs due to its role in platelet aggregation and blood clotting. While the importance of the P2Y₁₂R in the periphery has been known for decades, much less is known about its expression and roles in the central nervous system (CNS), where it is expressed exclusively on microglia - the first responders to brain insults and neurodegeneration. Several seminal studies have shown that P2Y₁₂ is a robust, translatable biomarker for anti-inflammatory and neuroprotective microglial phenotypes in models of degenerative diseases such as multiple sclerosis and Alzheimer's disease. An enduring problem for studying this receptor in vivo, however, is the lack of selective, high-affinity small molecule ligands that can bypass the blood-brain barrier and accumulate in the CNS. In this Digest, we discuss previous attempts by researchers to target the P2Y₁₂R in the CNS and opine on strategies that may be employed to design and assess the suitability of novel P2Y₁₂ ligands for this purpose going forward.

In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design

Positron emission tomography (PET) is a highly sensitive and versatile molecular imaging modality that leverages radiolabeled molecules, known as radiotracers, to interrogate biochemical processes such as metabolism, enzymatic activity, and receptor expression. The ability to probe specific molecular and cellular events longitudinally in a noninvasive manner makes PET imaging a particularly powerful technique for studying the central nervous system (CNS) in both health and disease. Unfortunately, developing and translating a single CNS PET tracer for clinical use is typically an extremely resource-intensive endeavor, often requiring synthesis and evaluation of numerous candidate molecules. While existing in vitro methods are beginning to address the challenge of derisking molecules prior to costly in vivo PET studies, most require a significant investment of resources and possess substantial limitations. In the context of CNS drug development, significant time and resources have been invested into the development and optimization of computational methods, particularly involving machine learning, to streamline the design of better CNS therapeutics. However, analogous efforts developed and validated for CNS radiotracer design are conspicuously limited. In this Perspective, we overview the requirements and challenges of CNS PET tracer design, survey the most promising computational methods for in silico CNS drug design, and bridge these two areas by discussing the potential applications and impact of computational design tools in CNS radiotracer design.

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning

The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure-activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient (R ²) and root mean square error (RMSE) were 0.625-0.669 and 0.295-0.318, respectively. We combined molecular descriptors and prediction probability of DeepSnap-DL as features and developed a novel regression method we called the combination model. In the combination model with these two types of features and conventional algorithms selected by DataRobot, R ² and RMSE were 0.710-0.769 and 0.247-0.278, respectively. This finding shows that the combination model performed better than molecular descriptor-based methods. Our combination model will contribute to the design of more rational compounds for drug discovery. This method may be applicable not only to rat CL but also to other pharmacokinetic and pharmacological activity and toxicity parameters; therefore, applying it to other parameters may help to accelerate drug discovery.

Pre- and post-treatment blood-based genomic landscape of patients with ROS1 or NTRK fusion-positive solid tumors treated with entrectinib

Genomic tumor profiling informs targeted treatment options. Entrectinib is a tyrosine kinase inhibitor with efficacy in NTRK fusion-positive (-fp) solid tumors and ROS1-fp non-small cell lung cancer. FoundationOne^® Liquid CDx (F1L CDx), a non-invasive in vitro next-generation sequencing (NGS)-based diagnostic, detects genomic alterations in plasma circulating tumor DNA (ctDNA). We evaluated the clinical validity of F1L CDx as an aid in identifying patients with NTRK-fp or ROS1-fp tumors, and assessed the genomic landscape pre- and post-entrectinib treatment. Among evaluable pre-treatment clinical samples (N = 85), positive percentage agreements between F1L CDx and clinical trial assays (CTAs) were 47.4% (NTRK fusions) and 64.5% (ROS1 fusions); positive predictive value was 100% for both. The objective response rate for CTA+ F1L CDx+ patients was 72.2% in both cohorts. The median duration of response significantly differed between F1L CDx+ and F1L CDx- samples in ROS1-fp (5.6 vs. 17.3 months) but not NTRK-fp (9.2 vs. 12.9 months) patients. Fifteen acquired resistance mutations were detected. We conclude that F1L CDx is a clinically valid complement to tissue-based testing to identify patients who may benefit from entrectinib and those with acquired resistance mutations associated with disease progression.

In-Vivo and Ex-Vivo Brain Uptake Studies of Peptidomimetic Neurolysin Activators in Healthy and Stroke Animals

PURPOSE

Neurolysin (Nln) is a peptidase that functions to preserve the brain following ischemic stroke by hydrolyzing various neuropeptides. Nln activation has emerged as an attractive drug discovery target for treatment of ischemic stroke. Among first-in-class peptidomimetic Nln activators, we selected three lead compounds (9d, 10c, 11a) for quantitative pharmacokinetic analysis to provide valuable information for subsequent preclinical development.

METHODS

Pharmacokinetic profile of these compounds was studied in healthy and ischemic stroke-induced mice after bolus intravenous administration. Brain concentration and brain uptake clearance (K_in) was calculated from single time point analysis. The inter-relationship between LogP with in-vitro and in-vivo permeability was studied to determine CNS penetration. Brain slice uptake method was used to study tissue binding, whereas P-gp-mediated transport was evaluated to understand the potential brain efflux of these compounds.

RESULTS

According to calculated parameters, all three compounds showed a detectable amount in the brain after intravenous administration at 4 mg/kg; however, 11a had the highest brain concentration and brain uptake clearance. A strong correlation was documented between in-vitro and in-vivo permeability data. The efflux ratio of 10c was ~6-fold higher compared to 11a and correlated well with its lower K_in value. In experimental stroke animals, the K_in of 11a was significantly higher in ischemic vs. contralateral and intact hemispheres, though it remained below its A₅₀ value required to activate Nln.

CONCLUSIONS

Collectively, these preclinical pharmacokinetic studies reveal promising BBB permeability of 11a and indicate that it can serve as an excellent lead for developing improved drug-like Nln activators.

Non-ionic Surfactants as a P-Glycoprotein(P-gp) Efflux Inhibitor for Optimal Drug Delivery-A Concise Outlook

Significant research efforts have been devoted to unraveling the mystery of P-glycoprotein(P-gp) in drug delivery applications. The efflux membrane transporter P-gp is widely distributed in the body and accountable for restricting drug absorption and bioavailability. For these reasons, it is the primary cause of developing multidrug resistance (MDR) in most drug delivery applications. Therefore, P-gp inhibitors must be explored to address MDR and the low bioavailability of therapeutic substrates. Several experimental models in kinetics and dynamic studies identified the sensitivity of drug molecules and excipients as a P-gp inhibitor. In this review, we aimed to emphasize nonionic surface-active agents for effective reversal of P-gp inhibition. As it is inert, non-toxic, noncharged, and quickly reaching the cytosolic lipid membrane (the point of contact with P-gp efflux protein) enables it to be more efficient as P-gp inhibitors. Moreover, nonionic surfactant improves drug absorption and bioavailability through the various mechanism, involving (i) association of drug with surfactant improves solubilization, facilitating its cell penetration and absorption; (ii) weakening the lateral membrane packing density, facilitating the passive drug influx; and (iii) inhibition of the ATP binding cassette of transporter P-glycoprotein. The application of nonionic surfactant as P-gp inhibitors is well established and supported by various experiments. Altogether, herein, we have primarily focused on various nonionic surfactants and their development strategies to conquer the MDR-causing effects of P-gp efflux protein in drug delivery. Graphical Abstract.

Pharmacoinformatics-based phytochemical screening for anticancer impacts of yellow sweet clover, Melilotus officinalis (Linn.) Pall

To date, much attention has been paid to phytochemicals because of their diverse pharmacological effects on a variety of diseases such as cancer. In this regard, computer-aided drug design, as a cost- and time-effective approach, is primarily applied to investigate the drug candidates before their further costly in vitro and in vivo experimental evaluations. Accordingly, different signaling pathways and proteins can be targeted using such strategies. As a key protein for the initiation of eukaryotic DNA replication, mini-chromosome maintenance complex component 7 (MCM7) overexpression is related to the initiation and progression of aggressive malignancies. The current study was conducted to identify new potential natural compounds from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, by examining the potential of 40 isolated phytochemicals against MCM7 protein. A structure-based pharmacophore model to the protein active site cavity was generated and followed by virtual screening and molecular docking. Overall, four compounds were selected for further evaluation based on their binding affinities. Our analyses revealed that two novel compounds, namely rosmarinic acid (PubChem CID:5281792) and melilotigenin (PubChem CID:14059499) might be druggable and offer safe usage in human. The stability of these two protein-ligand complex structures was confirmed through molecular dynamics simulation. The findings of this study reveal the potential of these two phytochemicals to serve as anticancer agents, while further pharmacological experiments are required to confirm their effectiveness against human cancers.

Strategies for Structural Modification of Small Molecules to Improve Blood-Brain Barrier Penetration: A Recent Perspective

In the development of central nervous system (CNS) drugs, the blood-brain barrier (BBB) restricts many drugs from entering the brain to exert therapeutic effects. Although many novel delivery methods of large molecule drugs have been designed to assist transport, small molecule drugs account for the vast majority of the CNS drugs used clinically. From this perspective, we review studies from the past five years that have sought to modify small molecules to increase brain exposure. Medicinal chemists make it easier for small molecules to cross the BBB by improving diffusion, reducing efflux, and activating carrier transporters. On the basis of their excellent work, we summarize strategies for structural modification of small molecules to improve BBB penetration. These strategies are expected to provide a reference for the future development of small molecule CNS drugs.

Prediction Model of Clearance by a Novel Quantitative Structure-Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

Some targets predicted by machine learning (ML) in drug discovery remain a challenge because of poor prediction. In this study, a new prediction model was developed and rat clearance (CL) was selected as a target because it is difficult to predict. A classification model was constructed using 1545 in-house compounds with rat CL data. The molecular descriptors calculated by Molecular Operating Environment (MOE), alvaDesc, and ADMET Predictor software were used to construct the prediction model. In conventional ML using 100 descriptors and random forest selected by DataRobot, the area under the curve (AUC) and accuracy (ACC) were 0.883 and 0.825, respectively. Conversely, the prediction model using DeepSnap and Deep Learning (DeepSnap-DL) with compound features as images had AUC and ACC of 0.905 and 0.832, respectively. We combined the two models (conventional ML and DeepSnap-DL) to develop a novel prediction model. Using the ensemble model with the mean of the predicted probabilities from each model improved the evaluation metrics (AUC = 0.943 and ACC = 0.874). In addition, a consensus model using the results of the agreement between classifications had an increased ACC (0.959). These combination models with a high level of predictive performance can be applied to rat CL as well as other pharmacokinetic parameters, pharmacological activity, and toxicity prediction. Therefore, these models will aid in the design of more rational compounds for the development of drugs.

Blood-brain barrier models: Rationale for selection

Brain delivery is a broad research area, the outcomes of which are far hindered by the limited permeability of the blood-brain barrier (BBB). Over the last century, research has been revealing the BBB complexity and the crosstalk between its cellular and molecular components. Pathologically, BBB alterations may precede as well as be concomitant or lead to brain diseases. To simulate the BBB and investigate options for drug delivery, several in vitro, in vivo, ex vivo, in situ and in silico models are used. Hundreds of drug delivery vehicles successfully pass preclinical trials but fail in clinical settings. Inadequate selection of BBB models is believed to remarkably impact the data reliability leading to unsatisfactory results in clinical trials. In this review, we suggest a rationale for BBB model selection with respect to the addressed research question and downstream applications. The essential considerations of an optimal BBB model are discussed.

Multi-Target Actions of Acridones from Atalantia monophylla towards Alzheimer's Pathogenesis and Their Pharmacokinetic Properties

Ten acridones isolated from Atalantia monophylla were evaluated for effects on Alzheimer’s disease pathogenesis including antioxidant effects, acetylcholinesterase (AChE) inhibition, prevention of beta-amyloid (Aβ) aggregation and neuroprotection. To understand the mechanism, the type of AChE inhibition was investigated in vitro and binding interactions between acridones and AChE or Aβ were explored in silico. Drug-likeness and ADMET parameters were predicted in silico using SwissADME and pKCSM programs, respectively. All acridones showed favorable drug-likeness and possessed multifunctional activities targeting AChE function, Aβ aggregation and oxidation. All acridones inhibited AChE in a mixed-type manner and bound AChE at both catalytic anionic and peripheral anionic sites. In silico analysis showed that acridones interfered with Aβ aggregation by interacting at the central hydrophobic core, C-terminal hydrophobic region, and the key residues 41 and 42. Citrusinine II showed potent multifunctional action with the best ADMET profile and could alleviate neuronal cell damage induced by hydrogen peroxide and Aβ_1-42 toxicity.

The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union

BACKGROUND

Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process.

METHODS

In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations.

RESULTS

As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information.

CONCLUSION

It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.

The effect of hypergravity in intestinal permeability of nanoformulations and molecules

The oral administration of drugs remains a challenge due to rapid enzymatic degradation and minimal absorption in the gastrointestinal tract. Mechanical forces, namely hypergravity, can interfere with cellular integrity and drug absorption, and there is no study describing its influence in the intestinal permeability. In this work, it was studied the effect of hypergravity on intestinal Caco-2 cells and its influence in the intestinal permeability of different nanoformulations and molecules. It was shown that the cellular metabolic activity and integrity were maintained after exposure to different gravity-levels (g-levels). Expression of important drug transporters and tight junctions' proteins was evaluated and, most proteins demonstrated a switch of behavior in their expression. Furthermore, paracellular transport of FITC-Dextran showed to significantly increase with hypergravity, which agrees with the decrease of transepithelial electrical resistance and the increase of claudin-2 at higher g-levels. The diffusion of camptothecin released from polymeric micelles revealed a significant decrease, which agrees with the increased expression of the P-gp observed with the increase in g-levels, responsible for pumping this drug out. The neonatal Fc receptor-mediated transport of albumin-functionalized nanoparticles loaded with insulin showed no significant changes when increasing the g-levels. Thus, this study supports the effect of hypergravity on intestinal permeability is dependent on the molecule studied and the mechanism by which it is absorbed in the intestine.

Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration

Developing in silico models to predict the brain penetration of drugs remains a challenge owing to the intricate involvement of multiple transport systems in the blood brain barrier, and the necessity to consider a combination of multiple pharmacokinetic parameters. P-glycoprotein (P-gp) is one of the most important transporters affecting the brain penetration of drugs. Here, we developed an in silico prediction model for P-gp efflux potential in brain capillary endothelial cells (BCEC). Using the representative values of P-gp net efflux ratio in BCEC, we proposed a novel prediction system for brain-to-plasma concentration ratio (Kp,brain) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) of P-gp substrates. We validated the proposed prediction system using newly acquired experimental brain penetration data of 28 P-gp substrates. Our system improved the predictive accuracy of brain penetration of drugs using only chemical structure information compared with that of previous studies.

Random Forest Model Prediction of Compound Oral Exposure in the Mouse

An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.

Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans

Abstract

Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure–PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings.

Graphical abstract

Supplementary informations

The online version of this article (10.1007/s11030-021-10186-7) contains supplementary material, which is available to authorized users.

Calculation of an Apical Efflux Ratio from P-Glycoprotein (P-gp) In Vitro Transport Experiments Shows an Improved Correlation with In Vivo Cerebrospinal Fluid Measurements in Rats: Impact on P-gp Screening and Compound Optimization

P-glycoprotein (P-gp) is a major blood-brain barrier (BBB) efflux transporter. In vitro approaches, including bidirectional efflux ratio (ER), are used to measure P-gp-mediated transport, but findings can be inconsistent across models. We propose a novel, more physiologically relevant, in vitro model: unidirectional apical efflux ratio (AP-ER)-a ratio of permeability rates at the apical side of the BBB with and without P-gp inhibitor. To test our approach, ER and AP-ER were calculated for 3227 structurally diverse compounds in porcine kidney epithelial cells (LLC-PK1) overexpressing human or mouse P-gp and classified based on their passive transcellular P-gp permeability or charged properties. In vivo rat infusion studies were performed for selected compounds with high ER but low AP-ER. One-third of the 3227 compounds had bidirectional ER that was much higher than AP-ER; very few had AP-ER higher than ER. Compounds with a large difference between AP-ER and ER were typically basic compounds with low-to-medium passive permeability and high lipophilicity and/or amphiphilicity, leading to strong membrane binding. Outcomes in the human model were similar to those in mice, suggesting AP-ER/ER ratios may be conserved for at least two species. AP-ER predicted measured cerebrospinal fluid (CSF) concentration better than ER for the five compounds tested in our in vivo rat infusion studies. We report superior estimations of the CSF concentrations of the compounds when based on less resource-intensive AP-ER versus classic ER. Better understanding of the properties leading to high P-gp-mediated efflux in vivo could support more efficient brain-penetrant compound screening and optimization. SIGNIFICANCE STATEMENT: To address inconsistencies associated with the historical, bidirectional efflux ratio (ER) calculation of P-glycoprotein-mediated transport, we propose to use the novel, more physiologically relevant, unidirectional apical efflux ratio (AP-ER) model. In vitro experiments suggested that compounds with strong membrane binding showed the largest difference between AP-ER and ER, and in vivo infusion studies showed that AP-ER predicted cerebrospinal fluid concentrations of compounds better than ER; outcomes in the human model were similar to those in mice.

Entrectinib, a TRK/ROS1 inhibitor with anti-CNS tumor activity: differentiation from other inhibitors in its class due to weak interaction with P-glycoprotein

BACKGROUND

Studies evaluating the CNS penetration of a novel tyrosine kinase inhibitor, entrectinib, proved challenging, particularly due to discrepancies across earlier experiments regarding P-glycoprotein (P-gp) interaction and brain distribution. To address this question, we used a novel "apical efflux ratio" (AP-ER) model to assess P-gp interaction with entrectinib, crizotinib, and larotrectinib, and compared their brain-penetration properties.

METHODS

AP-ER was designed to calculate P-gp interaction with the 3 drugs in vitro using P-gp-overexpressing cells. Brain penetration was studied in rat plasma, brain, and cerebrospinal fluid (CSF) samples after intravenous drug infusion. Unbound brain concentrations were estimated through kinetic lipid membrane binding assays and ex vivo experiments, while the antitumor activity of entrectinib was evaluated in a clinically relevant setting using an intracranial tumor mouse model.

RESULTS

Entrectinib showed lower AP-ER (1.1-1.15) than crizotinib and larotrectinib (≥2.8). Despite not reaching steady-state brain exposures in rats after 6 hours, entrectinib presented a more favorable CSF-to-unbound concentration in plasma (CSF/Cu,p) ratio (>0.2) than crizotinib and larotrectinib at steady state (both: CSF/Cu,p ~0.03). In vivo experiments validated the AP-ER approach. Entrectinib treatment resulted in strong tumor inhibition and full survival benefit in the intracranial tumor model at clinically relevant systemic exposures.

CONCLUSIONS

Entrectinib, unlike crizotinib and larotrectinib, is a weak P-gp substrate that can sustain CNS exposure based on our novel in vitro and in vivo experiments. This is consistent with the observed preclinical and clinical efficacy of entrectinib in neurotrophic tropomyosin receptor kinase (NTRK) and ROS1 fusion-positive CNS tumors and secondary CNS metastases.

Dual delivery nanoscale device for miR-451 and adriamycin co-delivery to combat multidrug resistant in bladder cancer

The outcome of current cancer therapy is usually impeded by complicated extracellular and intracellular barriers. Most importantly, untargeted distribution and multidrug resistance (MDR) are considered as two important difficulties responsible for the poor performance of many currently available drug delivery systems (DDS). As a result, in our study, we developed a cancer cell membrane (CM) coated calcium carbonate (CC) nanoparticles to co-delivery miR-451 with adriamycin (Adr) to address the dilemma occurred in the therapy of bladder cancer (MCC/R-A). The homologous CCM from MDR bladder cancer cells (BIU-87/Adr) was employed to increase targeted retention of DDS within the tumor tissue and to bypass the extracellular barriers. Moreover, the MDR of cancer cells was conquered through downregulation of P-gp expression using miR-451 since it was confirmed by previous reports that miR-451 could significantly downregulate the level of P-gp in MDR cells, which in turn elevated the cellular drug retention in BIU-87/Adr. Our in vitro and in vivo experiments have revealed that MCC/R-A showed a greatly enhanced therapeutic effect on BIU-87/Adr, which was superior than applying miR-451 or Adr alone. The preferable effect of MCC/R-A on conquering the MDR in bladder cancer provides a novel alternative for effective chemotherapy of MDR cancers.

An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology

Drug absorption is a complex process governed by a number of interrelated physicochemical, biopharmaceutical, and pharmacokinetic factors. In order to explore complex relationships among these factors, multivariate exploratory analysis was performed on the dataset of drugs with diverse bioperformance. The investigated dataset included subset of drugs for which bioequivalence between solid dosage form and oral solution has been reported, and subset of drugs described in the literature as low solubility/low permeability compounds. Discriminatory power of hierarchical clustering on principal components was somewhat higher when applied on the data subsets of drugs with similar bioperformance, while analysis of the integrated dataset indicated existence of two groups of drugs with the boundaries reflected in P_eff value of approximately 2 × 10^-4 cm/s and Fa and Fm values higher than 85% and 50%, respectively. Majority of the investigated drugs within the integrated dataset were grouped within their initial subset indicating that overall drug bioperformance is closely related to its physicochemical, biopharmaceutical and pharmacokinetic properties. Classification models constructed using the random forest (RF) and support vector machine with polynomial kernel function were able to predict food effect based on drug dose/solubility ratio (D/S), effective permeability (P_eff), percent of dose metabolized (Fm), and elimination half-life (τ_1/2). Although both models performed well during training and testing, only RF kept satisfying performance when applied on the external dataset (kappa value > 0.4). The results obtained indicate that data mining can be employed as useful tool in biopharmaceutical drug characterization which merits further investigation.

Cancer Cell Membrane Decorated Silica Nanoparticle Loaded with miR495 and Doxorubicin to Overcome Drug Resistance for Effective Lung Cancer Therapy

Current cancer therapy usually succumbs to many extracellular and intracellular barriers, among which untargeted distribution and multidrug resistance (MDR) are two important difficulties responsible for poor outcome of many drug delivery systems (DDS). Here, in our study, the dilemma was addressed by developing a cancer cell membrane (CCM)-coated silica (SLI) nanoparticles to co-deliver miR495 with doxorubicin (DOX) for effective therapy of lung cancer (CCM/SLI/R-D). The homologous CCM from MDR lung cancer cells (A549/DOX) was supposed to increase the tumor-homing property of the DDS to bypass the extracellular barriers. Moreover, the MDR of cancer cells were conquered through downregulation of P-glycoprotein (P-gp) expression using miR495. It was proved that miR495 could significantly decrease the expression of P-gp which elevated intracellular drug accumulation in A549/DOX. The in vitro and in vivo results exhibited that CCM/SLI/R-D showed a greatly enhanced therapeutic effect on A549/DOX, which was superior than applying miR495 or DOX alone. The preferable effect of CCM/SLI/R-D on conquering the MDR in lung cancer provides a novel alternative for effective chemotherapy of MDR cancers.

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking

P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to insufficient therapeutic targeting of tumor populations, representing a major drawback of cancer chemotherapy. Therefore, P-gp is a target for pharmacological inhibitors to overcome MDR. In the present study, we utilized machine learning strategies to establish a model for P-gp modulators to predict whether a given compound would behave as substrate or inhibitor of P-gp. Random forest feature selection algorithm-based leave-one-out random sampling was used. Testing the model with an external validation set revealed high performance scores. A P-gp modulator list of compounds from the ChEMBL database was used to test the performance, and predictions from both substrate and inhibitor classes were selected for the last step of validation with molecular docking. Predicted substrates revealed similar docking poses than that of doxorubicin, and predicted inhibitors revealed similar docking poses than that of the known P-gp inhibitor elacridar, implying the validity of the predictions. We conclude that the machine-learning approach introduced in this investigation may serve as a tool for the rapid detection of P-gp substrates and inhibitors in large chemical libraries.