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A computerized system and method for planning a medical treatment for an individual under specific medical condition comprises a data input utility configured for receiving input data, and a data processor. The input data includes first input data comprising medical data of a specific individual, and second input data comprising data indicative of at least one endpoint of treatment. The data processor is configured for utilizing the medical data of the specific individual and the data indicative of the at least one endpoint and processing data indicative of disease progression models, each disease progression model corresponding to a treatment plan comprising one or more predetermined treatment protocols for treating the specific medical condition. The data processor generates output data indicative of a treatment effect on the individual with respect to each of the treatment plans and at least one endpoint to evaluate the treatment plans.

Ribba B.,Ecole Normale Superieure de Lyon | Holford N.H.,Uppsala University | Magni P.,University of Pavia | Troconiz I.,University of Navarra | And 6 more authors.
CPT: Pharmacometrics and Systems Pharmacology | Year: 2014

Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology. © 2014 ASCPT All rights reserved.

Agency: European Commission | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2014-ETN | Award Amount: 3.61M | Year: 2014

Novel treatment options and associated personalised, patient-tailored therapies need to be explored and developed for highly heterogeneous and chemotherapy resistant cancers, such as malignant melanoma. This can only be achieved by industry-academia collaborations in newly emerging, innovative research disciplines such as translational cancer systems biology and systems medicine. These disciplines and the associated European training needs provide the foundation for the MEL-PLEX ETN. MEL-PLEX aims to understand the network-level and multi-scale regulation of disease-relevant signalling in melanoma through a combination of quantitative biomedical and computational research approaches that go significantly beyond the current state-of-the-art. Coordinated by the RCSI Centre for Systems Medicine, MEL-PLEX will train 15 early stage researchers through a highly interdisciplinary and intersectoral research training programme. MEL-PLEX comprises 11 beneficiaries and 7 partner organisations from 11 countries, including European and international leaders in personalised melanoma therapy, melanoma systems biology and cancer systems medicine. MEL-PLEX aims to (i) achieve an unmatched depth of molecular and mechanistic disease understanding, (ii) will exploit this knowledge to develop and validate predictive models for disease progression, prognosis and responsiveness to current and novel (co-)treatment options, and (iii) will provide superior and clinically relevant tools and biomarker signatures for personalising and optimising melanoma treatment. The MEL-PLEX ETN addresses current needs in academia and the private sector for researchers that have been trained in an environment that spans across biology, medicine and mathematics, that can navigate confidently between clinical, academic and private sector research environments, and that have developed an innovative and creative mindset to progress research findings towards applications.

Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.2.1.2-1 | Award Amount: 3.90M | Year: 2012

With the arrival of new colorectal cancer (CRC) therapeutics targeting specific cell signalling pathways, such as anti-EGFR therapy, personalised cancer treatment is at the door step of clinical practise. This progress in drug development contrasts strikingly with current clinical practice, where decision making depends largely on clinical factors such as tumour staging and age of patient, with the success of such treatments being largely unpredictable. 5-FU-based chemotherapy represents the main stay of CRC therapy. DNA damaging agents such as 5-FU and anti-EGFR therapy seek to induce tumour regression through induction of apoptosis or sensitization to apoptosis. Dysfunctional apoptosis is well recognized as a key contributing factor in chemotherapy resistance. The aim of the APODECIDE consortium is to develop systems medicine tools that predict treatment responses in CRC patients to 5-FU-based chemotherapy and anti-EGFR therapy, based on a systems analysis of apoptosis and EGFR signalling pathways. Based on previous clinical proof-of-concept studies that demonstrated the unique potential of such approaches in predicting tumour resistance, the APO-DECIDE consortium aims to deliver new clinical decision making tools that enable personalised medicine approaches and smart clinical trials design in the future. The SMEs will benefit from the project through the development of systems-based combinatorial biomarkers adapted to formalin fixed paraffin-embedded material, the routine material used in clinical histopathology, hence providing a unique opportunity for marketing and exploitation. SMEs and their academic partners will also develop computational whole body models reflecting drug pharmacodynamics and pharmacokinetics in patient cohorts, providing a unique market niche in the field clinical oncology.

Agency: European Commission | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2015-ETN | Award Amount: 3.65M | Year: 2015

Endoplasmic reticulum (ER) stress is emerging as a common feature in the pathology of numerous diseases including cancer, neurodegenerative disorders, metabolic syndromes and inflammatory diseases. Thus ER stress represents a potential therapeutic intervention point to be exploited to develop novel therapies, diagnostic tools and markers for these diseases. However, exploitation is hampered by the shortage of scientists with interdisciplinary training that can navigate with ease between the academic, industrial and clinical sectors, and that have the scientific and complementary skills, together with an innovative outlook, to convert research findings into commercial and clinical applications. This proposal will bring young researchers together with world-leading academics, clinicians and industry personnel, who are united in (1) their goal of forming a network of excellence aimed at understanding the ER stress response mechanistically and quantitatively and (2) applying this understanding to identify and validate the most suitable intervention points in order to provide innovative knowledge-driven strategies for the treatment of ER stress-associated diseases. The TRAIN-ERS network will provide early stage researchers (ESRs) with high quality scientific and complementary skills training combined with international, intersectoral work experience. This will produce highly trained, innovative, creative and entrepreneurial ESRs with greatly enhanced career prospects, who will continue to advance the state of the art in the Biomedical field in their further careers, and will confidently navigate at the interface of academic, clinical and private sector research. The TRAIN-ERS research programme will provide the ESRs with the knowledge and the cutting edge scientific and technical skills that will drive our understanding and exploitation of the ER stress response for therapeutic and diagnostic purposes.

Elishmereni M.,Institute for Medical Biomathematics IMBM | Kheifetz Y.,Institute for Medical Biomathematics IMBM | Sondergaard H.,Novo Nordisk AS | Overgaard R.V.,Novo Nordisk AS | And 2 more authors.
PLoS Computational Biology | Year: 2011

Interleukin (IL)-21 is an attractive antitumor agent with potent immunomodulatory functions. Yet thus far, the cytokine has yielded only partial responses in solid cancer patients, and conditions for beneficial IL-21 immunotherapy remain elusive. The current work aims to identify clinically-relevant IL-21 regimens with enhanced efficacy, based on mathematical modeling of long-term antitumor responses. For this purpose, pharmacokinetic (PK) and pharmacodynamic (PD) data were acquired from a preclinical study applying systemic IL-21 therapy in murine solid cancers. We developed an integrated disease/PK/PD model for the IL-21 anticancer response, and calibrated it using selected "training" data. The accuracy of the model was verified retrospectively under diverse IL-21 treatment settings, by comparing its predictions to independent "validation" data in melanoma and renal cell carcinoma-challenged mice (R 2&0.90). Simulations of the verified model surfaced important therapeutic insights: (1) Fractionating the standard daily regimen (50 μg/dose) into a twice daily schedule (25 μg/dose) is advantageous, yielding a significantly lower tumor mass (45% decrease); (2) A low-dose (12 μg/day) regimen exerts a response similar to that obtained under the 50 μg/day treatment, suggestive of an equally efficacious dose with potentially reduced toxicity. Subsequent experiments in melanoma-bearing mice corroborated both of these predictions with high precision (R 2&0.89), thus validating the model also prospectively in vivo. Thus, the confirmed PK/PD model rationalizes IL-21 therapy, and pinpoints improved clinically-feasible treatment schedules. Our analysis demonstrates the value of employing mathematical modeling and in silico-guided design of solid tumor immunotherapy in the clinic. © 2011 Elishmereni et al.

Agur Z.,Institute for Medical BioMathematics | Agur Z.,Optimata | Elishmereni M.,Institute for Medical BioMathematics | Elishmereni M.,Optimata | And 2 more authors.
Wiley Interdisciplinary Reviews: Systems Biology and Medicine | Year: 2014

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article. © 2014 Wiley Periodicals, Inc.

Provided is a system for recommending an optimal treatment protocol for an individual comprising: a system model; a plurality of treatment protocols; a system model modifier, wherein said system model is modified by the system model modifier based on parameters specific to the individual; and a selector to select an optimal treatment protocol from said plurality of treatment protocols based on the modified system model.

System for recommending an optimal treatment protocol for a specific individual are disclosed. The systems comprise generally a system model, a plurality of treatment protocols, a system model modifier, wherein said system model is modified by the system model modifier based on parameters specific to the individual; and a selector to select an optimal treatment protocol from said plurality of treatment protocols based on the modified system model. Systems embodying the above techniques but for a general patient are also disclosed. Systems for a general patient and an individual for various specific diseases are disclosed. Methods and computer program products embodying the above techniques are also disclosed.

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