Applying linear and non-linear methods for parallel prediction of volume of distribution and fraction of unbound drug

Species-Specific Unbound Fraction Differences in Highly Bound PFAS: A Comparative Study across Human, Rat, and Mouse Plasma and Albumin

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of fluorinated compounds which have yet to undergo comprehensive investigation regarding potential adverse health effects and bioaccumulative properties. With long half-lives and accumulative properties, PFAS have been linked to several toxic effects in both non-clinical species such as rat and mouse as well as human. Although biological impacts and specific protein binding of PFAS have been examined, there is no study focusing on the species-specific fraction unbound (fu) in plasma and related toxicokinetics. Herein, a presaturation equilibrium dialysis method was used to measure and validate the binding of 14 individual PFAS with carbon chains containing 4 to 12 perfluorinated carbon atoms and several functional head-groups to albumin and plasma of mouse (C57BL/6 and CD-1), rat, and human. Equivalence testing between each species-matrix combination showed positive correlation between rat and human when comparing fu in plasma and binding to albumin. Similar trends in binding were also observed for mouse plasma and albumin. Relatively high Spearman correlations for all combinations indicate high concordance of PFAS binding regardless of matrix. Physiochemical properties of PFAS such as molecular weight, chain length, and lipophilicity were found to have important roles in plasma protein binding of PFAS.

Biomedical Promise of Sustainable Microwave-Engineered Symmetric Curcumin Derivatives

Curcumin is a polyphenol of the Curcuma longa plant, which can be used for various medicinal purposes, such as inflammation and cancer treatment. In this context, two symmetric curcumin derivatives (D1-(1E,6E)-1,7-bis(4-acetamidophenyl)hepta-1,6-diene-3,5-dione and D2-p,p-dihydroxy di-cinnamoyl methane) were obtained by the microwave-based method and evaluated for their antitumoral effect on human cervix cancer in comparison with toxicity on non-tumoral cells, taking into account that they were predicted to act as apoptosis agonists or anti-inflammatory agents. The HeLa cell line was incubated for 24 and 72 h with a concentration of 50 μg/mL of derivatives that killed almost half of the cells compared to the control. In contrast, these compounds did not alter the viability of MRC-5 non-tumoral lung fibroblasts until 72 h of incubation. The nitric oxide level released by HeLa cells was higher compared to MRC-5 fibroblasts after the incubation with 100 μg/mL. Both derivatives induced the decrease of catalase activity and glutathione levels in cancer cells without targeting the same effect in non-tumoral cells. Furthermore, the Western blot showed an increased protein expression of HSP70 and a decreased expression of HSP60 and MCM2 in cells incubated with D2 compared to control cells. We noticed differences regarding the intensity of cell death between the tested derivatives, suggesting that the modified structure after synthesis can modulate their function, the most prominent effect being observed for sample D2. In conclusion, the outcomes of our in vitro study revealed that these microwave-engineered curcumin derivatives targeted tumor cells, much more specifically, inducing their death.

The promising impact of Bemcentinib and Repotrectinib on sleep impairment in Alzheimer's disease

Alzheimer's disease (AD), the most prevalent neurodegenerative disease, demands effective medication to alleviate symptoms. This study focused on sleep impairment as an overt clinical symptom and tauopathy as a prominent molecular symptom of this disease. Multiple compounds from three biomolecule libraries (719 compounds; ChemDiv:366 - ChEMBL:180 - PubChem:173) were evaluated for potential binding affinity and safety using AutoDock Vina and pkCSM, respectively, resulting in the selection of four candidate compounds (Lestaurtinib, Repotrectinib, Bemcentinib, and Zotiraciclib). Due to the similarity of Repotrectinib and Bemcentinib binding sites to ATP, 300 ns Martini 3 coarse-grained molecular dynamics (MD) was performed on these two molecules and ATP by NAMD. The stability of tau protein in the presence of drugs was assessed using a 200 ns Martini 3 MD simulation. Binding site analysis discloses Bemcentinib and Repotrectinib as two inhibitors occupying most amino acids in binding with ATP. The RMSD and RMS average correlation results revealed protein containing Bemcentinib and Repotrectinib to have a more stable state compared to ATP in the first 220 ns simulation. There was only a single detachment of Bemcentinib, while Repotrictinib detached twice at the end of the simulation. Eventually, adding Bemcentinib and Repotrectinib to the enzyme-tau complex significantly increased the number of tau detachments during the 200 ns simulation. We report Bemcentinib and Repotrectinib, formerly prescribed for cancer, as potential inhibitors of the CK1 δ. Besides their high binding affinity compared to ATP, they can inhibit all ATP-binding sites and alter the tau binding stability.Communicated by Ramaswamy H. Sarma.

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage

Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roche (9,685 unique compounds), we performed a proof-of-concept study to predict key PK properties from chemical structure alone, including plasma clearance (CLp), volume of distribution at steady-state (Vss), and oral bioavailability (F). Ten machine learning (ML) models were evaluated, including Single-Task, Multitask, and transfer learning approaches (i.e., pretraining with in vitro data). In addition to prediction accuracy, we emphasized human interpretability of outcomes, especially the quantification of uncertainty, applicability domains, and explanations of predictions in terms of molecular features. Results show that intravenous (IV) PK properties (CLp and Vss) can be predicted with good precision (average absolute fold error, AAFE of 1.96-2.84 depending on data split) and low bias (average fold error, AFE of 0.98-1.36), with AutoGluon, Gaussian Process Regressor (GP), and ChemProp displaying the best performance. Driven by higher complexity of oral PK studies, predictions of F were more challenging, with the best AAFE values of 2.35-2.60 and higher overprediction bias (AFE of 1.45-1.62). Multi-Task approaches and pretraining of ChemProp neural networks with in vitro data showed similar precision to Single-Task models but helped reduce the bias and increase correlations between observations and predictions. A combination of GP-computed prediction variance, molecular clustering, and dimensionality-reduction provided valuable quantitative insights into prediction uncertainty and applicability domains. SHAPley Additive exPlanations (SHAPs) highlighted molecular features contributing to prediction outcomes of Vss, providing explanations that could aid drug design. Combined results show that computational predictions of PK are feasible at the drug design stage, with several ML technologies converging to successfully leverage historical PK data sets. Further studies are needed to unlock the full potential of this approach, especially with respect to data set sizes and quality, transfer learning between in vitro and in vivo data sets, model-independent quantification of uncertainty, and explainability of predictions.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V_D,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V_D,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V_D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V_D,ss directly and to predict input parameters required for mechanistic and empirical V_D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V_D,ss predictions was also investigated. In predicting V_D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V_D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r² = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V_D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V_D,ss predictions significantly (81% within 3-fold, r² = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V_D,ss but was limited in estimating the compounds with low V_D,ss.

Evaluating the influence of half-life, milk:plasma partition coefficient, and volume of distribution on lactational exposure to chemicals in children

Women are exposed to multiple environmental chemicals, many of which are known to transfer to breast milk during lactation. However, little is known about the influence of the different chemical-specific pharmacokinetic parameters on children's lactational dose. Our objective was to develop a generic pharmacokinetic model and subsequently quantify the influence of three chemical-specific parameters (biological half-life, milk:plasma partition coefficient, and volume of distribution) on lactational exposure to chemicals and resulting plasma levels in children. We developed a two-compartment pharmacokinetic model to simulate lifetime maternal exposure, placental transfer, and lactational exposure to the child. We performed 10,000 Monte Carlo simulations where half-life, milk:plasma partition coefficient, and volume of distribution were varied. Children's dose and plasma levels were compared to their mother's by calculating child:mother dose ratios and plasma level ratios. We then evaluated the association between the three chemical-specific pharmacokinetic parameters and child:mother dose and level ratios through linear regression and decision trees. Our analyses revealed that half-life was the most influential parameter on children's lactational dose and plasma concentrations, followed by milk:plasma partition coefficient and volume of distribution. In bivariate regression analyses, half-life explained 72% of child:mother dose ratios and 53% of child:mother level ratios. Decision trees aiming to identify chemicals with high potential for lactational exposure (ratio>1) had an accuracy of 89% for child:mother dose ratios and 84% for child:mother level ratios. Our study showed the relative importance of half-life, milk:plasma partition coefficient, and volume of distribution on children's lactational exposure. Developed equations and decision trees will enable the rapid identification of chemicals with a high potential for lactational exposure.

In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.

pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

BACKGROUND

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values.

RESULTS

Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied.

CONCLUSIONS

Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Intravitreal clearance and volume of distribution of compounds in rabbits: In silico prediction and pharmacokinetic simulations for drug development

The aims of this research were to (1) create a curated universal database of intravitreal volumes of distribution (Vss, ivt) and clearances (CL ivt) of small molecular weight compounds and macromolecules and (2) to develop quantitative structure property relationship (QSPR) and pharmacokinetic models for the estimation of vitreal drug concentrations based on the compound structure. Vss, ivt and CL ivt values were determined from the available literature on intravitreal drug administration using compartmental models and curve fitting. A simple QSPR model for CL ivt of small molecular weight compounds was obtained with two descriptors: Log D7.4 and hydrogen bond donor capacity. The model predicted the internal and external test sets reliably with a mean fold error of 1.50 and 1.33, respectively (Q(2)Y=0.62). For 80% of the compounds the Vss, ivt was 1.18-2.28 ml; too narrow range for QSPR model building. Integration of the estimated Vss, ivt and predicted CL ivt parameters into pharmacokinetic simulation models allows prediction of vitreous drug concentrations after intravitreal administration. The present work presents for the first time a database of CL ivt and Vss, ivt values and the dependence of the CL ivt values on the molecular structure. The study provides also useful in silico tools to investigate a priori the intravitreal pharmacokinetic profiles for intravitreally injected candidate compounds and drug delivery systems.