A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug-induced toxicity.

We have built a quantitative systems toxicology modeling framework focused on the early prediction of oncotherapeutic-induced clinical intestinal adverse effects. The model describes stem and progenitor cell dynamics in the small intestinal epithelium and integrates heterogeneous epithelial-related processes, such as transcriptional profiles, citrulline kinetics, and probability of diarrhea. We fitted a mouse-specific version of the model to quantify doxorubicin and 5-fluorouracil (5-FU)-induced toxicity, which included pharmacokinetics and 5-FU metabolism and assumed that both drugs led to cell cycle arrest and apoptosis in stem cells and proliferative progenitors. The model successfully recapitulated observations in mice regarding dose-dependent disruption of proliferation which could lead to villus shortening, decrease of circulating citrulline, increased diarrhea risk, and transcriptional induction of the p53 pathway. Using a human-specific epithelial model, we translated the cytotoxic activity of doxorubicin and 5-FU quantified in mice into human intestinal injury and predicted with accuracy clinical diarrhea incidence. However, for gefitinib, a specific-molecularly targeted therapy, the mice failed to reproduce epithelial toxicity at exposures much higher than those associated with clinical diarrhea. This indicates that, regardless of the translational modeling approach, preclinical experimental settings have to be suitable to quantify drug-induced clinical toxicity with precision at the structural scale of the model. Our work demonstrates the usefulness of translational models at early stages of the drug development pipeline to predict clinical toxicity and highlights the importance of understanding cross-settings differences in toxicity when building these approaches.

Unravelling Mechanisms of Doxorubicin-Induced Toxicity in 3D Human Intestinal Organoids.

Doxorubicin is widely used in the treatment of different cancers, and its side effects can be severe in many tissues, including the intestines. Symptoms such as diarrhoea and abdominal pain caused by intestinal inflammation lead to the interruption of chemotherapy. Nevertheless, the molecular mechanisms associated with doxorubicin intestinal toxicity have been poorly explored. This study aims to investigate such mechanisms by exposing 3D small intestine and colon organoids to doxorubicin and to evaluate transcriptomic responses in relation to viability and apoptosis as physiological endpoints. The in vitro concentrations and dosing regimens of doxorubicin were selected based on physiologically based pharmacokinetic model simulations of treatment regimens recommended for cancer patients. Cytotoxicity and cell morphology were evaluated as well as gene expression and biological pathways affected by doxorubicin. In both types of organoids, cell cycle, the p53 signalling pathway, and oxidative stress were the most affected pathways. However, significant differences between colon and SI organoids were evident, particularly in essential metabolic pathways. Short time-series expression miner was used to further explore temporal changes in gene profiles, which identified distinct tissue responses. Finally, in silico proteomics revealed important proteins involved in doxorubicin metabolism and cellular processes that were in line with the transcriptomic responses, including cell cycle and senescence, transport of molecules, and mitochondria impairment. This study provides new insight into doxorubicin-induced effects on the gene expression levels in the intestines. Currently, we are exploring the potential use of these data in establishing quantitative systems toxicology models for the prediction of drug-induced gastrointestinal toxicity.

A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids.

Gefitinib is a tyrosine kinase inhibitor (TKI) that selectively inhibits the epidermal growth factor receptor (EGFR), hampering cell growth and proliferation. Due to its action, gefitinib has been used in the treatment of cancers that present abnormally increased expression of EGFR. However, side effects from gefitinib therapy may occur, among which diarrhoea is most common, that can lead to interruption of the planned therapy in the more severe cases. The mechanisms underlying intestinal toxicity induced by gefitinib are not well understood. Therefore, this study aims at providing insight into these mechanisms based on transcriptomic responses induced in vitro. A 3D culture of healthy human colon and small intestine (SI) organoids was exposed to 0.1, 1, 10 and 30 µM of gefitinib, for a maximum of three days. These drug concentrations were selected using physiologically-based pharmacokinetic simulation considering patient dosing regimens. Samples were used for the analysis of viability and caspase 3/7 activation, image-based analysis of structural changes, as well as RNA isolation and sequencing via high-throughput techniques. Differential gene expression analysis showed that gefitinib perturbed signal transduction pathways, apoptosis, cell cycle, FOXO-mediated transcription, p53 signalling pathway, and metabolic pathways. Remarkably, opposite expression patterns of genes associated with metabolism of lipids and cholesterol biosynthesis were observed in colon versus SI organoids in response to gefitinib. These differences in the organoids' responses could be linked to increased activated protein kinase (AMPK) activity in colon, which can influence the sensitivity of the colon to the drug. Therefore, this study sheds light on how gefitinib induces toxicity in intestinal organoids and provides an avenue towards the development of a potential tool for drug screening and development.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids.

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 µM. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development.

Merging systems biology with pharmacodynamics.

The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.

A control engineering approach to understanding the TGF-ß paradox in cancer.

TGF-ß, a key cytokine that regulates diverse cellular processes, including proliferation and apoptosis, appears to function paradoxically as a tumour suppressor in normal cells, and as a tumour promoter in cancer cells, but the mechanisms underlying such contradictory roles remain unknown. In particular, given that this cytokine is primarily a tumour suppressor, the conundrum of the unusually high level of TGF-ß observed in the primary cancer tissue and blood samples of cancer patients with the worst prognosis, remains unresolved. To provide a quantitative explanation of these paradoxical observations, we present, from a control theory perspective, a mechanistic model of TGF-ß-driven regulation of cell homeostasis. Analysis of the overall system model yields quantitative insight into how cell population is regulated, enabling us to propose a plausible explanation for the paradox: with the tumour suppressor role of TGF-ß unchanged from normal to cancer cells, we demonstrate that the observed increased level of TGF-ß is an effect of cancer cell phenotypic progression (specifically, acquired TGF-ß resistance), not the cause. We are thus able to explain precisely why the clinically observed correlation between elevated TGF-ß levels and poor prognosis is in fact consistent with TGF-ß's original (and unchanged) role as a tumour suppressor.

Optimization of the enzymatic one pot reaction for the synthesis of uridine 5'-diphosphogalactose.

Five recombinant Escherichia coli extracts harboring overexpressed galactokinase, galactose-1-phosphate uridyltransferase, UDP-glucose pyrophophorylase, UMP kinase, and acetate kinase (AK) were utilized for the production of UDP-galactose (UDP-Gal). We analyzed the parameters which limit the yield of UDP-Gal in the reaction, and the reaction was optimized by increasing the concentration of AK. AK was used for the ATP regeneration as well as the conversion of UDP to UTP. The activities of four overexpressed enzymes were identically fixed, and then we increased the activity of AK to 20 times higher than others. The extracts catalyzed the production of UDP-Gal from UMP (10 mM), galactose (12 mM), ATP (1 mM), and acetyl phosphate (40 mM). As the result of the reaction, the conversion yield of UDP-Gal reached to 95% from 10 mMUMP.

Quantitative modeling and analysis of the transforming growth factor beta signaling pathway.

Transforming growth factor beta (TGF-beta) signaling, which regulates multiple cellular processes including proliferation, apoptosis, and differentiation, plays an important but incompletely understood role in normal and cancerous tissues. For instance, although TGF-beta functions as a tumor suppressor in the premalignant stages of tumorigenesis, paradoxically, it also seems to act as a tumor promoter in advanced cancer leading to metastasis. The mechanisms by which TGF-beta elicits such diverse responses during cancer progression are still not entirely clear. As a first step toward understanding TGF-beta signaling quantitatively, we have developed a comprehensive, dynamic model of the canonical TGF-beta pathway via Smad transcription factors. By describing how an extracellular signal of the TGF-beta ligand is sensed by receptors and transmitted into the nucleus through intracellular Smad proteins, the model provides quantitative insight into how TGF-beta-induced responses are modulated and regulated. Subsequent model analysis shows that mechanisms associated with Smad activation by ligand-activated receptor, nuclear complex formation among Smad proteins, and inactivation of ligand-activated Smad (e.g., degradation, dephosphorylation) may be critical for regulating TGF-beta-targeted functional responses. The model was also used to predict dynamic characteristics of the Smad-mediated pathway in abnormal cells, from which we generated four testable hypotheses regarding potential mechanisms by which TGF-beta's tumor-suppressive roles may appear to morph into tumor-promotion during cancer progression.