Do TRPV1 antagonists increase the risk for skin tumourigenesis? A collaborative in vitro and in vivo assessment.

A recent hypothesis suggesting that the pharmacological target TRPV1 (transient receptor potential vanilloid subfamily, member 1) may function as a tumour suppressor, which potentially impacts the development of TRPV1 antagonist therapeutics for a range of conditions. However, little is known about the long-term physiologic effects of TRPV1 blockade in the skin. In vitro and in vivo studies suggested that the potent TRPV1 competitive antagonist AMG-9810 promoted proliferation in N/TERT1 cells (telomerase-immortalised primary human keratinocytes 1) and tumour development in mouse skin that was mediated through EGFR/Akt/mTOR signalling. We attempted to reproduce the reported in vitro and in vivo findings to further explore this hypothesis to understand the underlying mechanism and the risk associated with TRPV1 antagonism in the skin. In vitro proliferation studies using multiple methods and topical application with AMG-9810 and structurally similar TRPV1 antagonists such as SB-705498 and PAC-14028 were performed. Although we confirmed expression of TRPV1 in primary human epidermal keratinocytes (HEKn) and spontaneously immortalised human keratinocytes (HaCaT), we were unable to demonstrate cell proliferation in either cell type or any clear evidence of increased expression of proteins in the EGFR/Akt/mTOR signalling pathway with these molecules. We were also unable to demonstrate skin tumour promotion or underlying molecular mechanisms involved in the EGFR/Akt/mTOR signalling pathway in a single-dose and two-stage carcinogenesis mouse study treated with TRPV1 antagonists. In conclusion, our data suggest that inhibiting the pharmacological function of TRPV1 in skin by specific antagonists has not been considered to be indicative of skin tumour development.

Implementation of a Human Renal Proximal Tubule on a Chip for Nephrotoxicity and Drug Interaction Studies.

Proximal tubule epithelial cells (PTEC) are susceptible to drug-induced kidney injury (DIKI). Cell-based, two-dimensional (2D) in vitro PTEC models are often poor predictors of DIKI, probably due to the lack of physiological architecture and flow. Here, we assessed a high throughput, 3D microfluidic platform (Nephroscreen) for the detection of DIKI in pharmaceutical development. This system was established with four model nephrotoxic drugs (cisplatin, tenofovir, tobramycin and cyclosporin A) and tested with eight pharmaceutical compounds. Measured parameters included cell viability, release of lactate dehydrogenase (LDH) and N-acetyl-ß-d-glucosaminidase (NAG), barrier integrity, release of specific miRNAs, and gene expression of toxicity markers. Drug-transporter interactions for P-gp and MRP2/4 were also determined. The most predictive read outs for DIKI were a combination of cell viability, LDH and miRNA release. In conclusion, Nephroscreen detected DIKI in a robust manner, is compatible with automated pipetting, proved to be amenable to long-term experiments, and was easily transferred between laboratories. This proof-of-concept-study demonstrated the usability and reproducibility of Nephroscreen for the detection of DIKI and drug-transporter interactions. Nephroscreen it represents a valuable tool towards replacing animal testing and supporting the 3Rs (Reduce, Refine and Replace animal experimentation).

Recommended Guidelines for Developing, Qualifying, and Implementing Complex In Vitro Models (CIVMs) for Drug Discovery.

The pharmaceutical industry is continuing to face high research and development (R&D) costs and low overall success rates of clinical compounds during drug development. There is an increasing demand for development and validation of healthy or disease-relevant and physiological human cellular models that can be implemented in early-stage discovery, thereby shifting attrition of future therapeutics to a point in discovery at which the costs are significantly lower. There needs to be a paradigm shift in the early drug discovery phase (which is lengthy and costly), away from simplistic cellular models that show an inability to effectively and efficiently reproduce healthy or human disease-relevant states to steer target and compound selection for safety, pharmacology, and efficacy questions. This perspective article covers the various stages of early drug discovery from target identification (ID) and validation to the hit/lead discovery phase, lead optimization, and preclinical safety. We outline key aspects that should be considered when developing, qualifying, and implementing complex in vitro models (CIVMs) during these phases, because criteria such as cell types (e.g., cell lines, primary cells, stem cells, and tissue), platform (e.g., spheroids, scaffolds or hydrogels, organoids, microphysiological systems, and bioprinting), throughput, automation, and single and multiplexing endpoints will vary. The article emphasizes the need to adequately qualify these CIVMs such that they are suitable for various applications (e.g., context of use) of drug discovery and translational research. The article ends looking to the future, in which there is an increase in combining computational modeling, artificial intelligence and machine learning (AI/ML), and CIVMs.