A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro

Computational resources and chemoinformatics for translational health research

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Application of cell painting for chemical hazard evaluation in support of screening-level chemical assessments

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ~100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

"3D, human renal proximal tubule (RPTEC-TERT1) organoids 'tubuloids' for translatable evaluation of nephrotoxins in high-throughput"

The importance of human cell-based in vitro tools to drug development that are robust, accurate, and predictive cannot be understated. There has been significant effort in recent years to develop such platforms, with increased interest in 3D models that can recapitulate key aspects of biology that 2D models might not be able to deliver. We describe the development of a 3D human cell-based in vitro assay for the investigation of nephrotoxicity, using RPTEC-TERT1 cells. These RPTEC-TERT1 proximal tubule organoids 'tubuloids' demonstrate marked differences in physiologically relevant morphology compared to 2D monolayer cells, increased sensitivity to nephrotoxins observable via secreted protein, and with a higher degree of similarity to native human kidney tissue. Finally, tubuloids incubated with nephrotoxins demonstrate altered Na+/K+-ATPase signal intensity, a potential avenue for a high-throughput, translatable nephrotoxicity assay.

Generation and characterization of iPSC-derived renal proximal tubule-like cells with extended stability

The renal proximal tubule is responsible for re-absorption of the majority of the glomerular filtrate and its proper function is necessary for whole-body homeostasis. Aging, certain diseases and chemical-induced toxicity are factors that contribute to proximal tubule injury and chronic kidney disease progression. To better understand these processes, it would be advantageous to generate renal tissues from human induced pluripotent stem cells (iPSC). Here, we report the differentiation and characterization of iPSC lines into proximal tubular-like cells (PTL). The protocol is a step wise exposure of small molecules and growth factors, including the GSK3 inhibitor (CHIR99021), the retinoic acid receptor activator (TTNPB), FGF9 and EGF, to drive iPSC to PTL via cell stages representing characteristics of early stages of renal development. Genome-wide RNA sequencing showed that PTL clustered within a kidney phenotype. PTL expressed proximal tubular-specific markers, including megalin (LRP2), showed a polarized phenotype, and were responsive to parathyroid hormone. PTL could take up albumin and exhibited ABCB1 transport activity. The phenotype was stable for up to 7 days and was maintained after passaging. This protocol will form the basis of an optimized strategy for molecular investigations using iPSC derived PTL.

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Nephrotoxic antisense oligonucleotide SPC5001 induces kidney injury biomarkers in a proximal tubule-on-a-chip

Antisense oligonucleotides (ASOs) are a promising therapeutic modality. However, failure to predict acute kidney injury induced by SPC5001 ASO observed in a clinical trial suggests the need for additional preclinical models to complement the preceding animal toxicity studies. To explore the utility of in vitro systems in this space, we evaluated the induction of nephrotoxicity and kidney injury biomarkers by SPC5001 in human renal proximal tubule epithelial cells (HRPTEC), cultured in 2D, and in a recently developed kidney proximal tubule-on-a-chip. 2D HRPTEC cultures were exposed to the nephrotoxic ASO SPC5001 or the safe control ASO 556089 (0.16-40 µM) for up to 72 h, targeting PCSK9 and MALAT1, respectively. Both ASOs induced a concentration-dependent downregulation of their respective mRNA targets but cytotoxicity (determined by LDH activity) was not observed at any concentration. Next, chip-cultured HRPTEC were exposed to SPC5001 (0.5 and 5 µM) and 556089 (1 and 10 µM) for 48 h to confirm downregulation of their respective target transcripts, with 74.1 ± 5.2% for SPC5001 (5 µM) and 79.4 ± 0.8% for 556089 (10 µM). During extended exposure for up to 20 consecutive days, only SPC5001 induced cytotoxicity (at the higher concentration; 5 µM), as evaluated by LDH in the perfusate medium. Moreover, perfusate levels of biomarkers KIM-1, NGAL, clusterin, osteopontin and VEGF increased 2.5 ± 0.2-fold, 3.9 ± 0.9-fold, 2.3 ± 0.6-fold, 3.9 ± 1.7-fold and 1.9 ± 0.4-fold respectively, in response to SPC5001, generating distinct time-dependent profiles. In conclusion, target downregulation, cytotoxicity and kidney injury biomarkers were induced by the clinically nephrotoxic ASO SPC5001, demonstrating the translational potential of this kidney on-a-chip.

Stem cells based in vitro models: trends and prospects in biomaterials cytotoxicity studies

Advanced biomaterials are increasingly used for numerous medical applications from the delivery of cancer-targeted therapeutics to the treatment of cardiovascular diseases. The issues of foreign body reactions induced by biomaterials must be controlled for preventing treatment failure. Therefore, it is important to assess the biocompatibility and cytotoxicity of biomaterials on cell culture systems before proceeding to in vivo studies in animal models and subsequent clinical trials. Direct use of biomaterials on animals create technical challenges and ethical issues and therefore, the use of non-animal models such as stem cell cultures could be useful for determination of their safety. However, failure to recapitulate the complex in vivo microenvironment have largely restricted stem cell cultures for testing the cytotoxicity of biomaterials. Nevertheless, properties of stem cells such as their self-renewal and ability to differentiate into various cell lineages make them an ideal candidate for in vitro screening studies. Furthermore, the application of stem cells in biomaterials screening studies may overcome the challenges associated with the inability to develop a complex heterogeneous tissue using primary cells. Currently, embryonic stem cells, adult stem cells, and induced pluripotent stem cells are being used as in vitro preliminary biomaterials testing models with demonstrated advantages over mature primary cell or cell line based in vitro models. This review discusses the status and future directions of in vitro stem cell-based cultures and their derivatives such as spheroids and organoids for the screening of their safety before their application to animal models and human in translational research.

Freshly isolated primary human proximal tubule cells as an in vitro model for the detection of renal tubular toxicity

Drug induced kidney injury (DIKI) is a common reason for compound attrition in drug development pipelines with proximal tubule epithelial cells (PTECs) most commonly associated with DIKI. Here, we investigated freshly isolated human (hPTECs) as an in vitro model for assessing renal tubular toxicity. The freshly isolated hPTECs were first characterized to confirm gene expression of important renal transporters involved in drug handling which was further corroborated by confirming the functional activity of organic cation transporter 2 and organic anion transporter 1 by using transporter specific inhibitors. Additionally, functionality of megalin/cubilin endocytic receptors was also confirmed. A training set of 36 compounds was used to test the ability of the model to classify them using six different endpoints which included three biomarkers (Kidney Injury Molecule-1, Neutrophil gelatinase-associated lipocalin, and Clusterin) and three non-specific injury endpoints (ATP depletion, LDH leakage, and barrier permeability via transepithelial electrical resistance) in a dose-dependent manner across two independent kidney donors. In general, biomarkers showed higher predictivity than non-specific endpoints, with Clusterin showing the highest predictivity (Sensitivity/Specificity - 65.0/93.8 %). By using the thresholds generated from the training set, nine candidate internal Takeda compounds were screened where PTEC toxicity was identified as one of the findings in preclinical animal studies. The model correctly classified four of six true positives and two of three true negatives, showing validation of the in vitro model for detection of tubular toxicants. This work thus shows the potential application of freshly isolated primary hPTECs using translational biomarkers in assessment of tubular toxicity within the drug discovery pipeline.

High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model

Emerging data indicate that structural analogs of bisphenol A (BPA) such as bisphenol S (BPS), tetrabromobisphenol A (TBBPA), and bisphenol AF (BPAF) have been introduced into the market as substitutes for BPA. Our previous study compared in vitro testicular toxicity using murine C18-4 spermatogonial cells and found that BPAF and TBBPA exhibited higher spermatogonial toxicities as compared with BPA and BPS. Recently, we developed a novel in vitro three-dimensional (3D) testicular cell co-culture model, enabling the classification of reproductive toxic substances. In this study, we applied the testicular cell co-culture model and employed a high-content image (HCA)-based single-cell analysis to further compare the testicular toxicities of BPA and its analogs. We also developed a machine learning (ML)-based HCA pipeline to examine the complex phenotypic changes associated with testicular toxicities. We found dose- and time-dependent changes in a wide spectrum of adverse endpoints, including nuclear morphology, DNA synthesis, DNA damage, and cytoskeletal structure in a single-cell-based analysis. The co-cultured testicular cells were more sensitive than the C18 spermatogonial cells in response to BPA and its analogs. Unlike conventional population-averaged assays, single-cell-based assays not only showed the levels of the averaged population, but also revealed changes in the sub-population. Machine learning-based phenotypic analysis revealed that treatment of BPA and its analogs resulted in the loss of spatial cytoskeletal structure, and an accumulation of M phase cells in a dose- and time-dependent manner. Furthermore, treatment of BPAF-induced multinucleated cells, which were associated with altered DNA damage response and impaired cellular F-actin filaments. Overall, we demonstrated a new and effective means to evaluate multiple toxic endpoints in the testicular co-culture model through the combination of ML and high-content image-based single-cell analysis. This approach provided an in-depth analysis of the multi-dimensional HCA data and provided an unbiased quantitative analysis of the phenotypes of interest.