Translational Framework Predicting Tumour Response in Gemcitabine-Treated Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft Studies

The aim of this evaluation was to predict tumour response to gemcitabine in patients with advanced pancreas or ovarian cancer using pre-clinical data obtained from xenograft tumour-bearing mice. The approach consisted of building a translational model combining pre-clinical pharmacokinetic–pharmacodynamic (PKPD) models and parameters, with dosing paradigms used in the clinics along with clinical PK models to derive tumour profiles in humans driving overall survival. Tumour growth inhibition simulations were performed using drug effect parameters obtained from mice, system parameters obtained from mice after appropriate scaling, patient PK models for gemcitabine and carboplatin, and the standard dosing schedules given in the clinical scenario for both types of cancers. Tumour profiles in mice were scaled by body weight to their equivalent values in humans. As models for survival in humans showed that tumour size was the main driver of the hazard rate, it was possible to describe overall survival in pancreatic and ovarian cancer patients. Simulated tumour dynamics in pancreatic and ovarian cancer patients were evaluated using available data from clinical trials. Furthermore, calculated metrics showed values (maximal tumour regression [0–17%] and tumour size ratio at week 12 with respect to baseline [− 9, − 4.5]) in the range of those predicted with the clinical PKPD models. The model-informed Drug Discovery and Development paradigm has been successfully applied retrospectively to gemcitabine data, through a semi-mechanistic translational approach, describing the time course of the tumour response in patients from pre-clinical studies.Depto. de Farmacia Galénica y Tecnología AlimentariaFac. de FarmaciaTRUEpu

Predicting tumour growth and its impact on survival in gemcitabine-treated patients with advanced pancreatic cancer

The aim of this evaluation was to characterize the impact of the tumour size (TS) effects driven by the anticancer drug gemcitabine on overall survival (OS) in patients with advanced pancreatic cancer by building and validating a predictive semi-mechanistic joint TS-OS model. TS and OS data were obtained from one phase II and one phase III study where gemcitabine was administered (1000-1250 mg/kg over 30-60 min i.v infusion) as single agent to patients (n = 285) with advanced pancreatic cancer. Drug exposure, TS and OS were linked using the population approach with NONMEM 7.3. Pancreatic tumour progression was characterized by exponential growth (doubling time = 67 weeks), and tumour response to treatment was described as a function of the weekly area under the gemcitabine triphosphate concentration vs time curve (AUC), including treatment-related resistance development. The typical predicted percentage of tumour growth inhibition with respect to no treatment was 22.3% at the end of 6 chemotherapy cycles. Emerging resistance elicited a 57% decrease in drug effects during the 6th chemotherapy cycle. Predicted TS profile was identified as main prognostic factor of OS, with tumours responders' profiles improving median OS by 30 weeks compared to stable-disease TS profiles. Results of NCT00574275 trial were predicted using this modelling framework, thereby validating the approach as a prediction tool in clinical development. Our analyses show that despite the advanced stage of the disease in this patient population, the modelling framework herein can be used to predict the likelihood of treatment success using early clinical data.Innovative Medicine InitiativeEuropean Union's Seventh Framework ProgrammeDepto. de Farmacia Galénica y Tecnología AlimentariaFac. de FarmaciaTRUEpu

Embroidered electrodes for control of affordable myoelectric prostheses

The low-cost manufacturing and maintenance of prostheses is of vital importance to their successful deployment in developing countries. Low-cost prosthesis actuation is generally achieved by combining pre-programmed control strategies, with surface-electromyographic measurements taken from the residual limb. In a standard setting, these signals are measured with disposable gel electrodes. However, this limit on electrode reuse requires that prosthesis users have a stable supply of electrodes. Alternatively, the textile electrodes sewn from conductive thread are studied in the context of hand gesture recognition to consider their future use with low-cost prostheses. In this paper, it is demonstrated that textile electrodes can be applied for gesture recognition. To do so, surface electromyography (sEMG) experiments are run in South Africa on three amputees where they were asked to perform gestures with their phantom limb (i.e., the missing limb segment). A gesture recognition method is implemented, and the classification accuracy with data recorded from textile electrodes is compared to that from gel electrodes. Further analysis examining the relationship between classifier performance and physiological parameters are performed. Results show that textile electrodes can be used to perform accurate gesture recognition, and are comparable to disposable gel electrodes. This demonstrates that low-cost sensory systems are not barrier to myoelectric control in developing countries

Reducing the global environmental impacts of rapid infrastructure expansion

Infrastructures, such as roads, mines, and hydroelectric dams, are proliferating explosively. Often, this has serious direct and indirect environmental impacts. We highlight nine issues that should be considered by project proponents to better evaluate and limit the environmental risks of such developments

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n = 28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day−1. No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.Depto. de Farmacia Galénica y Tecnología AlimentariaFac. de FarmaciaTRUEpu

Phase 2 study of gandotinib (LY2784544) in patients with myeloproliferative neoplasms.

BACKGROUND: The Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) are associated with increases in janus kinase 2 (JAK2) signaling, often resulting from the JAK2 V617F mutation. LY2784544 (gandotinib) is a potent, selective, small-molecule inhibitor of JAK2 that has potential dose-dependent selectivity for the JAK2 V617F mutation and may inhibit additional JAK2 mutant isoforms in nonclinical testing. METHODS: A multicenter, single-arm, outpatient phase 2 study evaluated the efficacy, safety, and pharmacokinetics (PK) of gandotinib administered to patients (120 mg once daily) with MPNs, including polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF). Between May 2012 and March 2015, 138 patients received at least one dose of study drug. FINDINGS: Most frequent Grade 3 or 4 treatment-emergent adverse events that were considered study-drug related were anemia (11.6%), hyperuricemia (3.2%), fatigue (2.9%), diarrhea (2.2%), and thrombocytopenia (2.2%). Overall response rates (ORRs) in patients with JAK2 V617F-mutated PV, ET, and MF were 95%, 90.5%, and 9.1%, respectively, while patients with ET and MF without the JAK2 V617F mutations had ORRs of 43.7% and 0%, respectively. INTERPRETATIONS: LY2784544 demonstrated efficacy in JAK2 V617F-mutated MPNs, including in patients previously on ruxolitinib therapy, who had an ORR of 3.3%. At the 1-year visit, 44% of patients experienced a ≥50% improvement in the MPN-Symptom Assessment Form Total Symptom Score, and 26% of patients had a 50% reduction in Brief Fatigue Inventory score

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n = 28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day−1. No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.Depto. de Farmacia Galénica y Tecnología AlimentariaFac. de FarmaciaTRUEpu