Evaluation of ADMET PredictorTM in early discovery DMPK project work

Can membrane permeability of zwitterionic compounds be predicted by the solubility-diffusion model?

Zwitterions contain both positively and negatively charged functional groups, resulting in an overall net neutral charge. Nevertheless, the membrane permeability of the zwitterionic form of a compound is assumed to be much lower than the permeability of the uncharged neutral form. Although a significant proportion of pharmaceuticals are zwitterionic, it has not been clear so far whether their permeability is dominated by the permeation of the zwitterionic or the neutral form, since neutral fractions are often quite low as compared to the zwitterionic fraction. This complicates the in silico prediction of the permeability of zwitterionic compounds. In this work, we re-evaluated existing in vitro permeability data from literature measured with Caco-2/MDCK cell assays, using more strict exclusion criteria for effects like diffusion limitation by the aqueous boundary layers, paracellular transport, active transport and retention. Using this re-evaluated data set, we show that extracted intrinsic permeabilities of the neutral fraction are well predicted by the solubility-diffusion model (RMSE = 1.21; n = 18) if the permeability of the zwitterionic species is assumed negligible. Our work thus suggests that only the neutral species is relevant for the membrane permeability of zwitterionic compounds, and that membrane permeability of zwitterionic compounds is indeed predictable by the solubility-diffusion model.

A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans

PURPOSE

Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.

METHODS

This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a "bottom-up" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform's accuracy.

RESULTS

Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy.

CONCLUSION

The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.

Protective efficacy of nafronyl in diabetic retinopathy through targeted inhibition of key enzymes

Diabetic retinopathy is governed by abnormal apoptosis, increased capillary pressure, and other linked pathology that needs an efficient treatment by multitargeted approaches. Thus, the current study aimed to explore the potential of inhibition of targeted enzymes (DPP4, ACE-2, and aldose reductase) and free radical scavenging capabilities of selected compounds (nafronyl or naftidrofuryl) through in silico and in vivo investigations. Significant binding energies were observed in complexes of aldolase reductase, angiotensin type 1 receptor, and DPP4 against the nafronyl and sitagliptin more than -7.5 kcal/mol. Further validation of free energy was confirmed by calculations of molecular mechanics Poisson-Boltzmann surface area (MMPBSA), and configurational stabilities examined by PCA (principal component analysis). Additionally, drug-likeness was examined by the Swiss ADME web tool, which showed significant findings. Consequently, in vivo experimentations showed significant inflammation and alterations in retinal layers of inner plexiform (inner limiting membrane, nerve fibers, and ganglionic cells), inner nuclear layer (bipolar cells and horizontal cells), and photoreceptors cells. Whereas the treatments (nafronyl and sitagliptin) caused significant improvements in the histoarchitecture of the retina. Additionally, the HOMA indices (IR-insulin resistance, sensitivity, and β cells functioning) and levels of free radicals were significantly altered in the diabetic control group in comparison to intact control. Nafronyl administration showed significant ameliorations in HOMA indices as well as antioxidant levels. Based on the results, it can be concluded that nafronyl efficiently interacts with target enzymes, which may result in potent inhibition and ameliorations in retinal histology as well as glucose homeostasis and antioxidants.

Conventional vs Mechanistic IVIVC: A Comparative Study in Establishing Dissolution Safe Space for Extended Release Formulations

The use of in vitro-in vivo correlation (IVIVC) for extended release oral dosage forms is an important technique that can avoid potential clinical studies. IVIVC has been a topic of discussion over the past two decades since the inception of USFDA guidance. It has been routinely used for biowaivers, establishment of dissolution safe space and clinically relevant dissolution specifications, for supporting site transfers, scale-up and post approval changes. Although conventional or mathematical IVIVC is routinely used, other approach such as mechanistic IVIVC can be of attractive choice as it integrates all the physiological aspects. In the present study, we have performed comparative evaluation of mechanistic and conventional IVIVC for establishment of dissolution safe space using divalproex sodium and tofacitinib extended release formulations as case examples. Conventional IVIVC was established using Phoenix and mechanistic IVIVC was set up using Gastroplus physiologically based biopharmaceutics model (PBBM). Virtual dissolution profiles with varying release rates were constructed around target dissolution profile using Weibull function. After internal and external validation, the virtual dissolution profiles were integrated into mechanistic and conventional IVIVC and safe space was established by absolute error and T/R ratio's methods. The results suggest that mechanistic IVIVC yielded wider safe space as compared to conventional IVIVC. The results suggest that a mechanistic approach of establishing IVIVC may be a flexible approach as it integrates physiological aspects. These findings suggest that mechanistic IVIVC has wider potential as compared to conventional IVIVC to gain wider dissolution safe space and thus can avoid potential clinical studies.

Computer-aided drug discovery of c-Abl kinase inhibitors from plant compounds against chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a hematological malignancy characterized by the neoplastic transformation of hematopoietic stem cells, driven by the Philadelphia (Ph) chromosome resulting from a translocation between chromosomes 9 and 22. This Ph chromosome harbors the breakpoint cluster region (BCR) and the Abelson (ABL) oncogene (BCR-ABL1) which have a constitutive tyrosine kinase activity. However, the tyrosine kinase activity of BCR-ABL1 have been identified as a key player in CML initiation and maintenance through c-Abl kinase. Despite advancements in tyrosine kinase inhibitors, challenges such as efficacy, safety concerns, and recurring drug resistance persist. This study aims to discover potential c-Abl kinase inhibitors from plant compounds with anti-leukemic properties, employing drug-likeness assessment, molecular docking, in silico pharmacokinetics (ADMET) screening, density function theory (DFT), and molecular dynamics simulations (MDS). Out of 58 screened compounds for drug-likeness, 44 were docked against c-Abl kinase. The top hit compound (isovitexin) and nilotinib (control drug) were subjected to rigorous analyses, including ADMET profiling, DFT evaluation, and MDS for 100 ns. Isovitexin demonstrated a notable binding affinity (-15.492 kcal/mol), closely comparable to nilotinib (-16.826 kcal/mol), showcasing a similar binding pose and superior structural stability and reactivity. While these findings suggest isovitexin as a potential c-Abl kinase inhibitor, further validation through urgent in vitro and in vivo experiments is imperative. This research holds promise for providing an alternative avenue to address existing CML treatment and management challenges.Communicated by Ramaswamy H. Sarma.

Pitfalls in evaluating permeability experiments with Caco-2/MDCK cell monolayers

When studying the transport of molecules across biological membranes, intrinsic membrane permeability (P0) is more informative than apparent permeability (Papp), because it eliminates external (setup-specific) factors, provides consistency across experiments and mechanistic insight. It is thus an important building block for modeling the total permeability in any given scenario. However, extracting P0 is often difficult, if not impossible, when the membrane is not the dominant transport resistance. In this work, we set out to analyze Papp values measured with Caco-2/MDCK cell monolayers of 69 literature references. We checked the Papp values for a total of 318 different compounds for the extractability of P0, considering possible limitations by aqueous boundary layers, paracellular transport, recovery issues, active transport, a possible proton flux limitation, and sink conditions. Overall, we were able to extract 77 reliable P0 values, which corresponds to about one quarter of the total compounds analyzed, while about half were limited by the diffusion through the aqueous layers. Compared to an existing data set of P0 values published by Avdeef, our approach resulted in a much higher exclusion of compounds. This is a consequence of stricter compound- and reference-specific exclusion criteria, but also because we considered possible concentration-shift effects due to different pH values in the aqueous layers, an effect only recently described in literature. We thus provide a consistent and reliable set of P0, e.g. as a basis for future modeling.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

New Perspective for Using Antimicrobial and Cell-Penetrating Peptides to Increase Efficacy of Antineoplastic 5-FU in Cancer Cells

This study explores the effectiveness of the antineoplastic agent 5-FU in cancer cells by leveraging the unique properties of cationic antimicrobial peptides (CAMPs) and cell-penetrating peptides (CPPs). Traditional anticancer therapies face substantial limitations, including unfavorable pharmacokinetic profiles and inadequate specificity for tumor sites. These drawbacks often necessitate higher therapeutic agent doses, leading to severe toxicity in normal cells and adverse side effects. Peptides have emerged as promising carriers for targeted drug delivery, with their ability to selectively deliver therapeutics to cells expressing specific receptors. This enhances intracellular drug delivery, minimizes drug resistance, and reduces toxicity. In this research, we comprehensively evaluate the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of various AMPs and CPPs to gain insights into their potential as anticancer agents. The peptide synthesis involved a solid-phase synthesis using a Liberty Microwave Peptide Synthesizer. The peptide purity was confirmed via LC-MS and HPLC methods. For the ADMET screening, computational tools were employed, assessing parameters like absorption, distribution, metabolism, excretion, and toxicity. The cell lines A549 and UM-UC-5 were cultured and treated with 5-FU, CAMPs, and CPPs. The cell viability was measured using the MTT assay. The physicochemical properties analysis revealed favorable drug-likeness attributes. The peptides exhibited potential inhibitory activity against CYP3A4. The ADMET predictions indicated variable absorption and distribution characteristics. Furthermore, we assessed the effectiveness of these peptides alone and in combination with 5-FU, a widely used antineoplastic agent, in two distinct cancer cell lines, UM-UC-5 and A549. Our findings indicate that CAMPs can significantly reduce the cell viability in A549 cells, while CPPs exhibit promising results in UM-UC-5 cells. Understanding these multifaceted effects could open new avenues for antiviral and anticancer research. Further, experimental validation is necessary to confirm the mechanism of action of these peptides, especially in combination with 5-FU.

SHIP1 therapeutic target enablement: Identification and evaluation of inhibitors for the treatment of late-onset Alzheimer's disease

INTRODUCTION

The risk of developing Alzheimer's disease is associated with genes involved in microglial function. Inositol polyphosphate-5-phosphatase (INPP5D), which encodes Src homology 2 (SH2) domain-containing inositol polyphosphate 5-phosphatase 1 (SHIP1), is a risk gene expressed in microglia. Because SHIP1 binds receptor immunoreceptor tyrosine-based inhibitory motifs (ITIMs), competes with kinases, and converts PI(3,4,5)P3 to PI(3,4)P2, it is a negative regulator of microglia function. Validated inhibitors are needed to evaluate SHIP1 as a potential therapeutic target.

METHODS

We identified inhibitors and screened the enzymatic domain of SHIP1. A protein construct containing two domains was used to evaluate enzyme inhibitor potency and selectivity versus SHIP2. Inhibitors were tested against a construct containing all ordered domains of the human and mouse proteins. A cellular thermal shift assay (CETSA) provided evidence of target engagement in cells. Phospho-AKT levels provided further evidence of on-target pharmacology. A high-content imaging assay was used to study the pharmacology of SHIP1 inhibition while monitoring cell health. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties were evaluated to select a compound suitable for in vivo studies.

RESULTS

SHIP1 inhibitors displayed a remarkable array of activities and cellular pharmacology. Inhibitory potency was dependent on the protein construct used to assess enzymatic activity. Some inhibitors failed to engage the target in cells. Inhibitors that were active in the CETSA consistently destabilized the protein and reduced pAKT levels. Many SHIP1 inhibitors were cytotoxic either at high concentration due to cell stress or they potently induced cell death depending on the compound and cell type. One compound activated microglia, inducing phagocytosis at concentrations that did not result in significant cell death. A pharmacokinetic study demonstrated brain exposures in mice upon oral administration.

DISCUSSION

3-((2,4-Dichlorobenzyl)oxy)-5-(1-(piperidin-4-yl)-1H-pyrazol-4-yl) pyridine activated primary mouse microglia and demonstrated exposures in mouse brain upon oral dosing. Although this compound is our recommended chemical probe for investigating the pharmacology of SHIP1 inhibition at this time, further optimization is required for clinical studies.

Highlights

Cellular thermal shift assay (CETSA) and signaling (pAKT) assays were developed to provide evidence of src homology 2 (SH2) domain-contaning inositol phosphatase 1 (SHIP1) target engagement and on-target activity in cellular assays.A phenotypic high-content imaging assay with simultaneous measures of phagocytosis, cell number, and nuclear intensity was developed to explore cellular pharmacology and monitor cell health.SHIP1 inhibitors demonstrate a wide range of activity and cellular pharmacology, and many reported inhibitors are cytotoxic.The chemical probe 3-((2,4-dichlorobenzyl)oxy)-5-(1-(piperidin-4-yl)-1H-pyrazol-4-yl) pyridine is recommended to explore SHIP1 pharmacology.

Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4

BACKGROUND

Enhancing the precision of drug-drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (fm) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models.

RESEARCH DESIGN AND METHODS

The fm values were determined for 33 approved drugs using a human liver microsome for in vitro measurements and the ADMET Predictor software for in silico predictions. Subsequently, these fm values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUCcombo with ketoconazole/AUCdosing alone), and the accuracy of these predictions was evaluated by comparison with observed AUCR.

RESULTS

The integration of in vitro fm method demonstrates superior performance compared to the in silico fm method and fm of 100% method. Under the Guest-limits criteria, the integration of in vitro fm achieves an accuracy of 76%, while the in silico fm and fm of 100% methods achieve accuracies of 67% and 58%, respectively.

CONCLUSIONS

Our study highlights the importance of in vitro fm data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico fm in predicting DDIs.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Physiologically based pharmacokinetic modeling and simulation of cannabinoids in human plasma and tissues

There has been an increased public interest in developing consumer products containing nonintoxicating cannabinoids, such as cannabidiol (CBD) and cannabigerol (CBG). At the present time, there is limited information available on the pharmacokinetics of cannabinoids in humans. Since pharmacokinetic profiles are important in understanding the pharmacological and toxicological effects at the target sites, physiologically based pharmacokinetic (PBPK) modeling was used to predict the plasma and tissue concentrations of 17 cannabinoids in humans. PBPK models were established using measured (in vitro) and predicted (in silico) physicochemical and pharmacokinetic properties, such as water solubility and effective human jejunal permeability. Initially, PBPK models were established for CBD and the model performance was evaluated using reported clinical data after intravenous and oral administration. PBPK models were then developed for 16 additional cannabinoids including CBG, and the plasma and tissue concentrations were predicted after 30 mg oral administration. The pharmacokinetic profiles of the 16 cannabinoids were similar to CBD, and the plasma concentration and time profiles of CBD agreed well with clinical data in the literature. Although low exposure was predicted in the plasma (maximum plasma concentrations < 15 nM), the predicted tissue concentrations, especially the liver (maximum liver concentrations 70-183 nM), were higher after oral administration of 30 mg cannabinoids. These predicted plasma and tissue concentrations could be used to guide further in vitro and in vivo testing.

In Silico Analyses of a Promising Drug Candidate for the Treatment of Amyotrophic Lateral Sclerosis Targeting Superoxide Dismutase I Protein

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder in adults, which is associated with a highly disabling condition. To date, ALS remains incurable, and the only drugs approved by the FDA for its treatment confer a limited survival benefit. Recently, SOD1 binding ligand 1 (SBL-1) was shown to inhibit in vitro the oxidation of a critical residue for SOD1 aggregation, which is a central event in ALS-related neurodegeneration. In this work, we investigated the interactions between SOD1 wild-type and its most frequent variants, i.e., A4V (NP_000445.1:p.Ala5Val) and D90A (NP_000445.1:p.Asp91Val), with SBL-1 using molecular dynamics (MD) simulations. The pharmacokinetics and toxicological profile of SBL-1 were also characterized in silico. The MD results suggest that the complex SOD1-SBL-1 remains relatively stable and interacts within a close distance during the simulations. This analysis also suggests that the mechanism of action proposed by SBL-1 and its binding affinity to SOD1 may be preserved upon mutations A4V and D90A. The pharmacokinetics and toxicological assessments suggest that SBL-1 has drug-likeness characteristics with low toxicity. Our findings, therefore, suggested that SBL-1 may be a promising strategy to treat ALS based on an unprecedented mechanism, including for patients with these frequent mutations.

N-Derivatives of (Z)-Methyl 3-(4-Oxo-2-thioxothiazolidin-5-ylidene)methyl)-1H-indole-2-carboxylates as Antimicrobial Agents-In Silico and In Vitro Evaluation

Herein, we report the experimental evaluation of the antimicrobial activity of seventeen new (Z)-methyl 3-(4-oxo-2-thioxothiazolidin-5-ylidene)methyl)-1H-indole-2-carboxylate derivatives. All tested compounds exhibited antibacterial activity against eight Gram-positive and Gram-negative bacteria. Their activity exceeded those of ampicillin as well as streptomycin by 10-50 fold. The most sensitive bacterium was En. Cloacae, while E. coli was the most resistant one, followed by M. flavus. The most active compound appeared to be compound 8 with MIC at 0.004-0.03 mg/mL and MBC at 0.008-0.06 mg/mL. The antifungal activity of tested compounds was good to excellent with MIC in the range of 0.004-0.06 mg/mL, with compound 15 being the most potent. T. viride was the most sensitive fungal, while A. fumigatus was the most resistant one. Docking studies revealed that the inhibition of E. coli MurB is probably responsible for their antibacterial activity, while 14a-lanosterol demethylase of CYP51Ca is involved in the mechanism of antifungal activity. Furthermore, drug-likeness and ADMET profile prediction were performed. Finally, the cytotoxicity studies were performed for the most active compounds using MTT assay against normal MRC5 cells.

From traditional to data-driven medicinal chemistry: a case study

Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and effort until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and until experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data-driven approaches into practical medicinal chemistry and quantify the impact, as reported herein. Although the organization and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate the significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists. Keywords: medicinal chemistry; drug discovery; chemoinformatics; data science; data-driven R&D.