1
|
Guan R, Cai R, Guo B, Wang Y, Zhao C. A Data-Driven Computational Framework for Assessing the Risk of Placental Exposure to Environmental Chemicals. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7770-7781. [PMID: 38665120 DOI: 10.1021/acs.est.4c00475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework. These scores were then used to categorize the compounds into different levels of transplacental transport range, creating a gradient partition. These probability scores not only facilitated a more intuitive understanding of a compound's ability to cross the placental barrier but also provided valuable information for predicting potential placental disruptors. Compounds with probability scores greater than 90% were considered to have significant transplacental transport potential, whereas those with probability scores less than 80% were classified as unlikely to cross the placental barrier. Furthermore, external validation set results showed that the probability score could accurately predict the compounds known to cross the placental barrier. In conclusion, the computational framework proposed in this study enhances the intuitive understanding of the ability of compounds to cross the placental barrier and opens up new avenues for assessing the placental exposure risk of compounds.
Collapse
Affiliation(s)
- Ruining Guan
- School of Pharmacy, Lanzhou University, Lanzhou 730000, China
| | - Ruitong Cai
- School of Pharmacy, Lanzhou University, Lanzhou 730000, China
| | - Binbin Guo
- School of Pharmacy, Lanzhou University, Lanzhou 730000, China
| | - Yawei Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Chunyan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou 730000, China
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| |
Collapse
|
2
|
Gomatam A, Coutinho E. A chirality-sensitive approach to predict chemical transfer across the human placental barrier. Toxicol Lett 2024; 394:66-75. [PMID: 38423482 DOI: 10.1016/j.toxlet.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024]
Abstract
The placenta is a membrane that separates the fetus from the maternal circulation, and in addition to protecting the fetus, plays a key role in fetal growth and development. With increasing drug use in pregnancy, it is imperative that reliable models of estimating placental permeability and safety be established. In vitro methods and animal models such as rodent placenta are limited in application since the species-specific nature of the placenta prevents meaningful extrapolations to humans. In this regard, in silico approaches such as quantitative structure-property relationships (QSPRs) are useful alternatives. However, despite evidence that drug transport across the placenta is stereoselective (i.e., governed by the spatial arrangement of the atoms in a molecule), many QSPR models for placental transfer have been built using 2D descriptors that do not account for chirality and stereochemistry. In this study, we apply a chirality-sensitive and proven QSPR methodology titled "EigenValue ANalySis" (EVANS) to build QSPR models for placental transfer. We deploy EVANS along with robust machine learning algorithms to build (i) regression models on a dataset of environmental chemicals (dataset PD I) followed by (ii) classification models on a set of drug-like compounds (dataset PD II). The best models were found to achieve state-of-the-art performance, with the support vector machine algorithm returning rtrain2=0.85,rtest2=0.75 for PD I, and the logistic regression algorithm giving accuracy 0.88 and F1 score 0.93 for PD II. The best models were interpreted with the Shapley Additive Explanations paradigm, and it was found that autocorrelation descriptors are crucial for modelling placental permeability. In conclusion, we demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals, and present two predictive QSPR models that may reliably be used for prediction of placental transfer.
Collapse
Affiliation(s)
- Anish Gomatam
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Mumbai, India.
| | - Evans Coutinho
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Mumbai, India; St. John Institute of Pharmacy and Research, Palghar (E), Palghar, India
| |
Collapse
|
3
|
Sobańska AW. In silico assessment of risks associated with pesticides exposure during pregnancy. CHEMOSPHERE 2023; 329:138649. [PMID: 37043889 DOI: 10.1016/j.chemosphere.2023.138649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 05/03/2023]
Abstract
Novel Quantitative Structure-Activity Relationship (QSAR) models of compounds' placenta (PL) permeability expressed as their log FM (fetus-to-mother blood concentration) values or binary PL1/0 (crossing/non-crossing) score were generated using a number of statistical tools: Multiple Linear Regression, Boosted Trees, Principal Component Analysis and Artificial Neural Networks, on the basis of molecular descriptors calculated by Mordred software and selected using Partial Least Squares (PLS) analysis. It was established that the most important predictor of both log FM and the binary PL1/0 score is Lipinski - a binary variable reflecting the compounds' ability to satisfy the criteria of drug-likeness according to the Lipinski's "Rule of 5". The quantitative (log FM) and qualitative (PL1/0) models of PL permeability were applied to 345 pesticides from different chemical families (triazines, carbamates, pyrethroids, organochlorine, organophosphorus and miscellaneous compounds). The ability of studied pesticides to cross the placenta was assessed; the basic physico-chemical parameters responsible for good or poor placenta transport of pesticides were identified and the relationships between the pesticides' PL permeability, blood-brain barrier (BBB) transfer and gastro-intestinal (GI) absorption were investigated. It was found (on the basis of logistic regression analysis) that the probability of a compound crossing the placenta (PL1) is inversely correlated with its lipophilicity and molar refractivity and positively correlated with the total count of oxygen and nitrogen atoms.
Collapse
Affiliation(s)
- Anna W Sobańska
- Department of Analytical Chemistry Medical University of Lodz, 90-151, Łódź, Muszyńskiego 1, Poland.
| |
Collapse
|
4
|
Maass C, Schaller S, Dallmann A, Bothe K, Müller D. Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study. Toxicol Sci 2023; 192:59-70. [PMID: 36637193 PMCID: PMC10025876 DOI: 10.1093/toxsci/kfad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.
Collapse
Affiliation(s)
| | | | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen 51373, Germany
| | - Kathrin Bothe
- Regulatory Toxicology, Research and Development, Bayer AG, CropScience, 40789 Monheim am Rhein, Germany
| | - Dennis Müller
- Regulatory Toxicology, Research and Development, Bayer AG, CropScience, 40789 Monheim am Rhein, Germany
| |
Collapse
|
5
|
Chandrasekar V, Ansari MY, Singh AV, Uddin S, Prabhu KS, Dash S, Khodor SA, Terranegra A, Avella M, Dakua SP. Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta. IEEE ACCESS 2023; 11:52726-52739. [DOI: 10.1109/access.2023.3272987] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2023]
Affiliation(s)
| | | | | | - Shahab Uddin
- Hamad Medical Corporation, Translational Research Institute, Academic Health System, Doha, Qatar
| | - Kirthi S. Prabhu
- Hamad Medical Corporation, Translational Research Institute, Academic Health System, Doha, Qatar
| | - Sagnika Dash
- Department of Obstetrics and Gynecology, Apollo Clinic, Doha, Qatar
| | - Souhaila Al Khodor
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | - Annalisa Terranegra
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | - Matteo Avella
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | | |
Collapse
|
6
|
Luconi M, Sogorb MA, Markert UR, Benfenati E, May T, Wolbank S, Roncaglioni A, Schmidt A, Straccia M, Tait S. Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto-Placental Organ-on-Chip Platform. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15828. [PMID: 36497907 PMCID: PMC9737555 DOI: 10.3390/ijerph192315828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Developmental toxicity testing urgently requires the implementation of human-relevant new approach methodologies (NAMs) that better recapitulate the peculiar nature of human physiology during pregnancy, especially the placenta and the maternal/fetal interface, which represent a key stage for human lifelong health. Fit-for-purpose NAMs for the placental-fetal interface are desirable to improve the biological knowledge of environmental exposure at the molecular level and to reduce the high cost, time and ethical impact of animal studies. This article reviews the state of the art on the available in vitro (placental, fetal and amniotic cell-based systems) and in silico NAMs of human relevance for developmental toxicity testing purposes; in addition, we considered available Adverse Outcome Pathways related to developmental toxicity. The OECD TG 414 for the identification and assessment of deleterious effects of prenatal exposure to chemicals on developing organisms will be discussed to delineate the regulatory context and to better debate what is missing and needed in the context of the Developmental Origins of Health and Disease hypothesis to significantly improve this sector. Starting from this analysis, the development of a novel human feto-placental organ-on-chip platform will be introduced as an innovative future alternative tool for developmental toxicity testing, considering possible implementation and validation strategies to overcome the limitation of the current animal studies and NAMs available in regulatory toxicology and in the biomedical field.
Collapse
Affiliation(s)
- Michaela Luconi
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
- I.N.B.B. (Istituto Nazionale Biostrutture e Biosistemi), Viale Medaglie d’Oro 305, 00136 Rome, Italy
| | - Miguel A. Sogorb
- Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, 03202 Elche, Spain
| | - Udo R. Markert
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Tobias May
- InSCREENeX GmbH, Inhoffenstr. 7, 38124 Braunschweig, Germany
| | - Susanne Wolbank
- Ludwig Boltzmann Institut for Traumatology, The Research Center in Cooperation with AUVA, Austrian Cluster for Tissue Regeneration, Donaueschingenstrasse 13, 1200 Vienna, Austria
| | - Alessandra Roncaglioni
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Astrid Schmidt
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Marco Straccia
- FRESCI by Science&Strategy SL, C/Roure Monjo 33, Vacarisses, 08233 Barcelona, Spain
| | - Sabrina Tait
- Centre for Gender-Specific Medicine, Istituto Superiore di Sanità, 00161 Rome, Italy
| |
Collapse
|
7
|
Abrahamsson D, Siddharth A, Robinson JF, Soshilov A, Elmore S, Cogliano V, Ng C, Khan E, Ashton R, Chiu WA, Fung J, Zeise L, Woodruff TJ. Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS). JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:808-819. [PMID: 36207486 PMCID: PMC9742309 DOI: 10.1038/s41370-022-00481-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 05/10/2023]
Abstract
BACKGROUND Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus. OBJECTIVE The aim of our study is to develop a computational approach that can be used to evaluate the of extend to which small molecules, and in particular PFAS, can cross to cross the placenta and partition to cord blood. METHODS We collected experimental values of the concentration ratio between cord and maternal blood (RCM) for 260 chemical compounds and calculated their physicochemical descriptors using the cheminformatics package Mordred. We used the compiled database to, train and test an artificial neural network (ANN). And then applied the best performing model to predict RCM for a large dataset of PFAS chemicals (n = 7982). We, finally, examined the calculated physicochemical descriptors of the chemicals to identify which properties correlated significantly with RCM. RESULTS We determined that 7855 compounds were within the applicability domain and 127 compounds are outside the applicability domain of our model. Our predictions of RCM for PFAS suggested that 3623 compounds had a log RCM > 0 indicating preferable partitioning to cord blood. Some examples of these compounds were bisphenol AF, 2,2-bis(4-aminophenyl)hexafluoropropane, and nonafluoro-tert-butyl 3-methylbutyrate. SIGNIFICANCE These observations have important public health implications as many PFAS have been shown to interfere with fetal development. In addition, as these compounds are highly persistent and many of them can readily cross the placenta, they are expected to remain in the population for a long time as they are being passed from parent to offspring. IMPACT Understanding the behavior of chemicals in the human body during pregnancy is critical in preventing harmful exposures during critical periods of development. Many chemicals can cross the placenta and expose the fetus, however, the mechanism by which this transport occurs is not well understood. In our study, we developed a machine learning model that describes the transplacental transfer of chemicals as a function of their physicochemical properties. The model was then used to make predictions for a set of 7982 per- and polyfluorinated alkyl substances that are listed on EPA's CompTox Chemicals Dashboard. The model can be applied to make predictions for other chemical categories of interest, such as plasticizers and pesticides. Accurate predictions of RCM can help scientists and regulators to prioritize chemicals that have the potential to cause harm by exposing the fetus.
Collapse
Affiliation(s)
- Dimitri Abrahamsson
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA.
| | - Adi Siddharth
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA
| | - Joshua F Robinson
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA
| | - Anatoly Soshilov
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Sarah Elmore
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Vincent Cogliano
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Carla Ng
- Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA, 15261, USA
| | - Elaine Khan
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Randolph Ashton
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, 330 N Orchard St, Madison, WI, 53715, USA
- The Stem Cell and Regenerative Medicine Center, University of Wisconsin, Madison, 1111 Highland Avenue, Madison, WI, 53705, USA
- Department of Biomedical Engineering, University of Wisconsin - Madison, 1550 Engineering Drive, Madison, WI, 53706, USA
| | - Weihsueh A Chiu
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Jennifer Fung
- Department of Obstetrics, Gynecology, and Reproductive Science and the Center of Reproductive Science, University of California, San Francisco, San Francisco, CA, 94143-2240, USA
| | - Lauren Zeise
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Tracey J Woodruff
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA.
| |
Collapse
|
8
|
Chou CY, Lin P, Kim J, Wang SS, Wang CC, Tung CW. Ensemble learning for predicting ex vivo human placental barrier permeability. BMC Bioinformatics 2022; 22:629. [PMID: 36138350 PMCID: PMC9502578 DOI: 10.1186/s12859-022-04937-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable. Methods A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability. Results Among the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments. Conclusion In this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04937-y.
Collapse
Affiliation(s)
- Che-Yu Chou
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Pinpin Lin
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Jongwoon Kim
- Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, Republic of Korea
| | - Shan-Shan Wang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, Taiwan.
| | - Chun-Wei Tung
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan. .,Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, Taiwan.
| |
Collapse
|
9
|
van Hoogdalem MW, Wexelblatt SL, Akinbi HT, Vinks AA, Mizuno T. A review of pregnancy-induced changes in opioid pharmacokinetics, placental transfer, and fetal exposure: Towards fetomaternal physiologically-based pharmacokinetic modeling to improve the treatment of neonatal opioid withdrawal syndrome. Pharmacol Ther 2021; 234:108045. [PMID: 34813863 DOI: 10.1016/j.pharmthera.2021.108045] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/29/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling has emerged as a useful tool to study pharmacokinetics (PK) in special populations, such as pregnant women, fetuses, and newborns, where practical hurdles severely limit the study of drug behavior. PK in pregnant women is variable and everchanging, differing greatly from that in their nonpregnant female and male counterparts typically enrolled in clinical trials. PBPK models can accommodate pregnancy-induced physiological and metabolic changes, thereby providing mechanistic insights into maternal drug disposition and fetal exposure. Fueled by the soaring opioid epidemic in the United States, opioid use during pregnancy continues to rise, leading to an increased incidence of neonatal opioid withdrawal syndrome (NOWS). The severity of NOWS is influenced by a complex interplay of extrinsic and intrinsic factors, and varies substantially between newborns, but the extent of prenatal opioid exposure is likely the primary driver. Fetomaternal PBPK modeling is an attractive approach to predict in utero opioid exposure. To facilitate the development of fetomaternal PBPK models of opioids, this review provides a detailed overview of pregnancy-induced changes affecting the PK of commonly used opioids during gestation. Moreover, the placental transfer of these opioids is described, along with their disposition in the fetus. Lastly, the implementation of these factors into PBPK models is discussed. Fetomaternal PBPK modeling of opioids is expected to provide improved insights in fetal opioid exposure, which allows for prediction of postnatal NOWS severity, thereby opening the way for precision postnatal treatment of these vulnerable infants.
Collapse
Affiliation(s)
- Matthijs W van Hoogdalem
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH, USA
| | - Scott L Wexelblatt
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Henry T Akinbi
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Alexander A Vinks
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Tomoyuki Mizuno
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
| |
Collapse
|
10
|
Costa J, Mackay R, de Aguiar Greca SC, Corti A, Silva E, Karteris E, Ahluwalia A. The Role of the 3Rs for Understanding and Modeling the Human Placenta. J Clin Med 2021; 10:jcm10153444. [PMID: 34362227 PMCID: PMC8347836 DOI: 10.3390/jcm10153444] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/28/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Modeling the physiology of the human placenta is still a challenge, despite the great number of scientific advancements made in the field. Animal models cannot fully replicate the structure and function of the human placenta and pose ethical and financial hurdles. In addition, increasingly stricter animal welfare legislation worldwide is incentivizing the use of 3R (reduction, refinement, replacement) practices. What efforts have been made to develop alternative models for the placenta so far? How effective are they? How can we improve them to make them more predictive of human pathophysiology? To address these questions, this review aims at presenting and discussing the current models used to study phenomena at the placenta level: in vivo, ex vivo, in vitro and in silico. We describe the main achievements and opportunities for improvement of each type of model and critically assess their individual and collective impact on the pursuit of predictive studies of the placenta in line with the 3Rs and European legislation.
Collapse
Affiliation(s)
- Joana Costa
- Centro di Ricerca E.Piaggio, University of Pisa, 56126 Pisa, Italy; (J.C.); (A.C.)
| | - Ruth Mackay
- Centre for Genome Engineering and Maintenance, Department of Mechanical and Aerospace Engineering, Brunel University London, Uxbridge UB8 3PH, UK;
| | | | - Alessandro Corti
- Centro di Ricerca E.Piaggio, University of Pisa, 56126 Pisa, Italy; (J.C.); (A.C.)
- Department of Translational Medicine, University of Pisa, 56126 Pisa, Italy
| | - Elisabete Silva
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK; (S.-C.d.A.G.); (E.S.); (E.K.)
| | - Emmanouil Karteris
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK; (S.-C.d.A.G.); (E.S.); (E.K.)
| | - Arti Ahluwalia
- Centro di Ricerca E.Piaggio, University of Pisa, 56126 Pisa, Italy; (J.C.); (A.C.)
- Department of Information Engineering, University of Pisa, 56122 Pisa, Italy
- Interuniversity Centro for the Promotion of 3Rs Principles in Teaching and Research (Centro3R), Italy
- Correspondence:
| |
Collapse
|
11
|
Di Filippo JI, Bollini M, Cavasotto CN. A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier. Front Chem 2021; 9:714678. [PMID: 34354979 PMCID: PMC8329444 DOI: 10.3389/fchem.2021.714678] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/07/2021] [Indexed: 12/05/2022] Open
Abstract
The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns.
Collapse
Affiliation(s)
- Juan I Di Filippo
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Argentina.,Facultad de Ciencias Biomédicas and Facultad de Ingeniería, Universidad Austral, Pilar, Argentina.,Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina
| | - Mariela Bollini
- Centro de Investigaciones en BioNanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Argentina.,Facultad de Ciencias Biomédicas and Facultad de Ingeniería, Universidad Austral, Pilar, Argentina.,Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina
| |
Collapse
|
12
|
Passarin PBS, Lourenço FR. Modeling an in silico platform to predict chromatographic profiles of UV filters using ChromSimulator. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
13
|
Comparative study of machine learning approaches for classification and prediction of selective caspase-3 antagonist for Zika virus drugs. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04626-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
14
|
Wang CC, Lin P, Chou CY, Wang SS, Tung CW. Prediction of human fetal-maternal blood concentration ratio of chemicals. PeerJ 2020; 8:e9562. [PMID: 32742813 PMCID: PMC7380269 DOI: 10.7717/peerj.9562] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/26/2020] [Indexed: 12/13/2022] Open
Abstract
Background The measurement of human fetal-maternal blood concentration ratio (logFM) of chemicals is critical for the risk assessment of chemical-induced developmental toxicity. While a few in vitro and ex vivo experimental methods were developed for predicting logFM of chemicals, the obtained experimental results are not able to directly predict in vivo outcomes. Methods A total of 55 chemicals with logFM values representing in vivo fetal-maternal blood ratio were divided into training and test datasets. An interpretable linear regression model was developed along with feature selection methods. Cross-validation on training dataset and prediction on independent test dataset were conducted to validate the prediction model. Results This study presents the first valid quantitative structure-activity relationship model following the Organisation for Economic Co-operation and Development (OECD) guidelines based on multiple linear regression for predicting in vivo logFM values. The autocorrelation descriptor AATSC1c and information content descriptor ZMIC1 were identified as informative features for predicting logFM. After the adjustment of the applicability domain, the developed model performs well with correlation coefficients of 0.875, 0.850 and 0.847 for model fitting, leave-one-out cross-validation and independent test, respectively. The model is expected to be useful for assessing human transplacental exposure.
Collapse
Affiliation(s)
- Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, Taiwan
| | - Pinpin Lin
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Che-Yu Chou
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Shan-Shan Wang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Chun-Wei Tung
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan.,Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| |
Collapse
|
15
|
Dobričić V, Turković N, Ivković B, Csuvik O, Vujić Z. Evaluation of the lipophilicity of chalcones by RP-TLC and computational methods. JPC-J PLANAR CHROMAT 2020. [DOI: 10.1007/s00764-020-00029-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
16
|
Liu XI, Momper JD, Rakhmanina NY, Green DJ, Burckart GJ, Cressey TR, Mirochnick M, Best BM, van den Anker JN, Dallmann A. Prediction of Maternal and Fetal Pharmacokinetics of Dolutegravir and Raltegravir Using Physiologically Based Pharmacokinetic Modeling. Clin Pharmacokinet 2020; 59:1433-1450. [PMID: 32451908 DOI: 10.1007/s40262-020-00897-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND Predicting drug pharmacokinetics in pregnant women including placental drug transfer remains challenging. This study aimed to develop and evaluate maternal-fetal physiologically based pharmacokinetic models for two antiretroviral drugs, dolutegravir and raltegravir.
Collapse
Affiliation(s)
- Xiaomei I Liu
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.
| | - Jeremiah D Momper
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Natella Y Rakhmanina
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Elizabeth Glaser Pediatric AIDS Foundation, Washington, DC, USA
| | - Dionna J Green
- Office of Pediatric Therapeutics, US Food and Drug Administration, Silver Spring, MD, USA
| | - Gilbert J Burckart
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, USA
| | - Tim R Cressey
- PHPT/IRD 174, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | | | - Brookie M Best
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - John N van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Division of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - André Dallmann
- Division of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
- Clinical Pharmacometrics, Bayer, Leverkusen, Germany
| |
Collapse
|
17
|
Codaccioni M, Bois F, Brochot C. Placental transfer of xenobiotics in pregnancy physiologically-based pharmacokinetic models: Structure and data. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.100111] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
18
|
Leonard JA. Supporting systems science through in silico applications: A focus on informing metabolic mechanisms. CURRENT OPINION IN TOXICOLOGY 2019. [DOI: 10.1016/j.cotox.2019.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
19
|
Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String. J CHEM-NY 2019. [DOI: 10.1155/2019/9858371] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.
Collapse
|
20
|
In Vitro Models for Studying Transport Across Epithelial Tissue Barriers. Ann Biomed Eng 2018; 47:1-21. [DOI: 10.1007/s10439-018-02124-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 08/28/2018] [Indexed: 12/16/2022]
|