Vera-Yunca, D. (Diego)
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- Disease pharmacokinetic–pharmacodynamic modelling in acute intermittent porphyria to support the development of mRNA-based therapies(2020) Parra-Guillen, Z.P. (Zinnia Patricia); Martini, P. (Paolo); Guey, L. T. (Lin); Hard, M. (Marjie); Fernández-de-Trocóniz, J.I. (José Ignacio); Fontanellas-Romá, A. (Antonio); Vera-Yunca, D. (Diego); Jericó-Asenjo, D. (Daniel); Jiang, L. (Lei)Background and Purpose Acute intermittent porphyria (AIP) results from haplo-insufficiency of the porphobilinogen deaminase (PBGD) gene encoding the third enzyme in the haem biosynthesis pathway. As liver is the main organ of pathology for AIP, emerging therapies that restore enzyme hepatic levels are appealing. The objective of this work was to develop a mechanistic-based computational framework to describe the effects of novel PBGD mRNA therapy on the accumulation of neurotoxic haem precursors in small and large animal models. Experimental Approach Liver PBGD activity data and/or 24-hr urinary haem precursors were obtained from genetic AIP mice and wild-type mice, rats, rabbits, and macaques. To mimic acute attacks, porphyrogenic drugs were administered over one or multiple challenges, and animals were used as controls or treated with different PBGD mRNA products. Available experimental data were sequentially used to build and validate a semi-mechanistic mathematical model using non-linear mixed-effects approach. Key Results The developed framework accounts for the different biological processes involved (i.e., mRNA sequence, release from lipid nanoparticle and degradation, mRNA translation, increased PBGD activity in liver, and haem precursor metabolism) in a simplified mechanistic fashion. The model, validated using external data, shows robustness in the extrapolation of PBGD activity data in rat, rabbit, and non-human primate species. Conclusion and Implications This quantitative framework provides a valuable tool to compare PBGD mRNA drug products during early preclinical stages, optimize the amount of experimental data required, and project results to humans, thus supporting drug development and clinical dose and dosing regimen selection.
- Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival(2020) Parra-Guillen, Z.P. (Zinnia Patricia); Girard, P. (Pascal.); Terranova, N. (Nadia); Troconiz, I.F. (Iñaki F.); Munafo, A. (Alain); Vera-Yunca, D. (Diego)Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient’s CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08–1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions’ TS dynamics could improve oncology models in favor of a better prediction of OS.
- Mechanistic modelling of enzyme-restoration effects of new recombinant liver-targeted proteins in acute intermittent porphyria(Wiley, 2022) Parra-Guillen, Z.P. (Zinnia Patricia); Troconiz, I.F. (Iñaki F.); Córdoba, K.M. (Karol M.); Fontanellas-Romá, A. (Antonio); Vera-Yunca, D. (Diego); Jericó-Asenjo, D. (Daniel)Background and Purpose:Acute intermittent porphyria (AIP) is a rare disease causedby a genetic mutation in the hepatic activity of the porphobilinogen-deaminase. Weaimed to develop a mechanistic model of the enzymatic restoration effects of a noveltherapy based on the administration of different formulations of recombinanthuman-PBGD (rhPBGD) linked to the ApoAI lipoprotein. This fusion protein circu-lates in blood, incorporating into HDL and penetrating hepatocytes.Experimental Approach:Single i.v. dose of different formulations of rhPBGD linkedto ApoAI were administered to AIP mice in which a porphyric attack was triggered byi.p. phenobarbital. Data consist on 24 h urine excreted amounts of heme precursors,5-aminolevulinic acid (ALA), PBG and total porphyrins that were analysed using non-linear mixed-effects analysis.Key Results:The mechanistic model successfully characterized over time theamounts excreted in urine of the three heme precursors for different formulations ofrhPBGD and unravelled several mechanisms in the heme pathway, such as the regu-lation in ALA synthesis by heme. Treatment with rhPBGD formulations restoredPBGD activity, increasing up to 51 times the value of the rate of tPOR formationestimated from baseline. Model-based simulations showed that several formulationprototypes provided efficient protective effects when administered up to 1 weekprior to the occurrence of the AIP attack.Conclusion and Implications:The model developed had excellent performance overa range of doses and formulation type. This mechanistic model warrants use beyondApoAI-conjugates and represents a useful tool towards more efficient drug treat-ments of other enzymopenias as well as for acute intermittent porphyria.
- Development of disease and drug models for rare diseases: Application to Acute Intermittent Porphyria.(Universidad de Navarra, 2022-01-13) Vera-Yunca, D. (Diego); Parra-Guillen, Z.P. (Zinnia Patricia); Troconiz, I.F. (Iñaki F.)This thesis illustrates an evolving mechanistic framework developed for Acute Intermittent Porphyria (AIP) at the discovery and pre-clinical stages of development. This type of approach provides quantitative support for already established mechanisms of the biological system involved, allows the search for new pathways, and incorporates drug effects on the specific target(s). We believe that the information gathered during the present investigation will help the research and development of innovative therapies for AIP and beyond. The current thesis has been organized as follows: The Introduction section briefly describes rare diseases and the challenges of designing clinical trials for this kind of diseases. Then, it is focused on the different modeling approaches applied to rare diseases caused by a genetic mutation along the gene expression process, ending with some considerations for the use of modeling in translational approaches from preclinical to clinical studies. Chapter 1 presents to the best of our knowledge the first computational model developed for AIP mice. The urinary excretion of heme precursors, the biomarkers of AIP, in porphyric mice during phenobarbital-induced acute attacks were well described by the proposed disease progression model which infers the heme biosynthesis pathway and the processes occurring in liver and blood. Model parameters were accurately estimated, and part of the data was used to validate externally the model. A theoretical simulation was performed by adding the effect of the standard-of-care for AIP, hemin, to demonstrate the potential capabilities of this mechanistic framework Chapter 2 expands this mechanistic modeling framework by including data from an innovative therapy, a mRNA encoding a human porphobilinogen deaminase (PBGD), which is the mutated enzyme in AIP. The kinetics of the different mRNA formulations, the dynamics of the heme precursors and the effects of the exogenous PBGD on restoring normal levels of these biomarkers were adequately predicted by the model. Data came from different animal species, and several mRNA formulations were investigated allowing the estimation of formulation- and animal-specific parameters, which facilitated the projections of mRNA formulations to humans in a search for an optimal dosing scenario. Chapter 3 shows the most developed AIP model up to date. It described well the urinary excretion of the heme precursors over time for control mice and for those that were treated with a new recombinant PBGD modified to target the liver. The fact that the experimental setting included three different formulations, several dose levels and the intravenous and subcutaneous routes of administration allowed the characterization of regulatory mechanisms that remained hidden during the previous analyses described in chapters 1 and 2 above, such as the inhibitory effects of heme on the ALA synthase enzyme at the start of the heme biosynthesis pathway, or the different molecular processes that cause acute attack induction by phenobarbital. The protective effects of the different recombinant PBGD formulations were evaluated by simulating different experimental scenarios in which the therapies were administered at different times prior to the induction of the AIP attack. The General Discussion section highlights the main aspects of the three chapters and integrates them with the considerations considered in the Introduction section. Finally, the last section, Conclusions, presents a summary of the main results of this thesis.