Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective optimization of New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline, namely, the presence of uncertainty, the high level of the involved capital costs, the interdependency between projects, the limited availability of resources, the overwhelming number of decisions due to the length of the time horizon (about 10 years) and the combinatorial nature of a portfolio. Formally, the NPD problem can be stated as follows: select a set of R and D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while copying with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGA II type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. An object-oriented model previously developed for batch plant scheduling and design is then extended to embed the case of new product management, which is particularly adequate for reuse of both structure and logic. Two case studies illustrate and validate the approach. From this simulation study, three performance evaluation criteria must be considered for decision making: the Net Present Value (NPV) of a sequence, its associated risk defined as the number of positive occurrences of NPV among the samples and the time to market. Theyv have been used in the multiobjective optimization formulation of the problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. NSGA II has been adapted to the treated case for taking into account both the number of products in a sequence and the drug release order. From an analysis performed for a representative case study on the different pairs of criteria both for the bi- and tricriteria optimization, the optimization strategy turns out to be efficient and particularly elitist to detect the sequences which can be considered by the decision makers. Only a few sequences are detected. Among theses sequences, large portfolios cause resource queues and delays time to launch and are eliminated by the bicriteria optimization strategy. Small portfolio reduces queuing and time to launch appear as good candidates. The optimization strategy is interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Optimisation du développement de nouveaux produits dans l'industrie pharmaceutique par algorithme génétique multicritère

Le développement de nouveaux produits constitue une priorité stratégique de l'industrie pharmaceutique, en raison de la présence d'incertitudes, de la lourdeur des investissements mis en jeu, de l'interdépendance entre projets, de la disponibilité limitée des ressources, du nombre très élevé de décisions impliquées dû à la longueur des processus (de l'ordre d'une dizaine d'années) et de la nature combinatoire du problème. Formellement, le problème se pose ainsi : sélectionner des projets de Ret D parmi des projets candidats pour satisfaire plusieurs critères (rentabilité économique, temps de mise sur le marché) tout en considérant leur nature incertaine. Plus précisément, les points clés récurrents sont relatifs à la détermination des projets à développer une fois que les molécules cibles sont identifiées, leur ordre de traitement et le niveau de ressources à affecter. Dans ce contexte, une approche basée sur le couplage entre un simulateur à événements discrets stochastique (approche Monte Carlo) pour représenter la dynamique du système et un algorithme d'optimisation multicritère (de type NSGA II) pour choisir les produits est proposée. Un modèle par objets développé précédemment pour la conception et l'ordonnancement d'ateliers discontinus, de réutilisation aisée tant par les aspects de structure que de logique de fonctionnement, a été étendu pour intégrer le cas de la gestion de nouveaux produits. Deux cas d'étude illustrent et valident l'approche. Les résultats de simulation ont mis en évidence l'intérêt de trois critères d'évaluation de performance pour l'aide à la décision : le bénéfice actualisé d'une séquence, le risque associé et le temps de mise sur le marché. Ils ont été utilisés dans la formulation multiobjectif du problème d'optimisation. Dans ce contexte, des algorithmes génétiques sont particulièrement intéressants en raison de leur capacité à conduire directement au front de Pareto et à traiter l'aspect combinatoire. La variante NSGA II a été adaptée au problème pour prendre en compte à la fois le nombre et l'ordre de lancement des produits dans une séquence. A partir d'une analyse bicritère réalisée pour un cas d'étude représentatif sur différentes paires de critères pour l'optimisation bi- et tri-critère, la stratégie d'optimisation s'avère efficace et particulièrement élitiste pour détecter les séquences à considérer par le décideur. Seules quelques séquences sont détectées. Parmi elles, les portefeuilles à nombre élevé de produits provoquent des attentes et des retards au lancement ; ils sont éliminés par la stratégie d'optimistaion bicritère. Les petits portefeuilles qui réduisent les files d'attente et le temps de lancement sont ainsi préférés. Le temps se révèle un critère important à optimiser simultanément, mettant en évidence tout l'intérêt d'une optimisation tricritère. Enfin, l'ordre de lancement des produits est une variable majeure comme pour les problèmes d'ordonnancement d'atelier. ABSTRACT : New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline, namely, the presence of uncertainty, the high level of the involved capital costs, the interdependency between projects, the limited availability of resources, the overwhelming number of decisions due to the length of the time horizon (about 10 years) and the combinatorial nature of a portfolio. Formally, the NPD problem can be stated as follows: select a set of R and D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while copying with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGA II type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. An object-oriented model previously developed for batch plant scheduling and design is then extended to embed the case of new product management, which is particularly adequate for reuse of both structure and logic. Two case studies illustrate and validate the approach. From this simulation study, three performance evaluation criteria must be considered for decision making: the Net Present Value (NPV) of a sequence, its associated risk defined as the number of positive occurrences of NPV among the samples and the time to market. Theyv have been used in the multiobjective optimization formulation of the problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. NSGA II has been adapted to the treated case for taking into account both the number of products in a sequence and the drug release order. From an analysis performed for a representative case study on the different pairs of criteria both for the bi- and tricriteria optimization, the optimization strategy turns out to be efficient and particularly elitist to detect the sequences which can be considered by the decision makers. Only a few sequences are detected. Among theses sequences, large portfolios cause resource queues and delays time to launch and are eliminated by the bicriteria optimization strategy. Small portfolio reduces queuing and time to launch appear as good candidates. The optimization strategy is interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

El análisis del riesgo como medida del capital económico

La gestión de riesgos y de capitales es un marco unificado que combina estos aspectos a través del uso del capital económico como una forma común para medir el riesgo. El capital económico es el límite de capital necesario para hacer frente a potenciales pérdidas inherentes a las actividades comerciales. El objetivo de este trabajo teórico es identificar la tipología de riesgos y cuantificarlos, mediante el Best Estimate y el Risk Margin.The risk management and of capitals he is a frame united that he combines these aspects through the use of economic capital like a common form to measure risk. The economic capital is the limit of necessary capital to face up to potentials inherent losses to the commercial activities. In this research our objective is to determine the different risk types and quantify it, by the Best Estimate and the Risk Margin

Thermoelectric Energy Harvesting for Oxygen Determination in Refrigerated Intelligent Packaging

In this paper, we present a passive tag for the determination of gaseous oxygen in intelligent packaging (IP). The power supply for this tag is obtained from thermoelectric energy harvesting taking advantage of the temperature difference between a cooled package and the human body. For this purpose, a compact Peltier module is attached to the surface of the pack7 age. This device is able to generate 1.2 mW when a temperature difference of 25 °C is applied between its surfaces. A dc-to-dc boost converter is included to generate an output voltage of 3.3 V and an output current of 225 µA from the harvested energy by the Peltier cell, which are used to supply the measurement circuitry. A luminescent membrane sensitive to oxygen is used as a gas detector in the package. The generated signal is compared to a reference value to evaluate if the oxygen concentration inside the package falls below or above a predetermined value. This is shown by turning on a green or a red LED, respectively. The proposed system presents a resolution of 0.02% of the predicted oxygen concentration in the range of interest (0%–5%) and a limit of detection (LOD) of 0.007%, which makes the instrument appropriate to be used in IP and active packaging (AP) technology.This work was supported in part by the Spanish Ministry of Economics and Competivity under Project CTQ2016-78754-C2-1-R and in part by the Unidad de Excelencia de Química aplicada a biomedicina y medioambiente, University of Granada. The work of P. E. Araque was supported by the Spanish Ministry of Education, Culture and Sport (MECD) under Grant FPU13/05032. The work of I. M. P. de Vargas-Sansalvador was supported by the European Unions Horizon 2020 research and innovation program under Grant 706303 (MultiSens

Impacto del World Padel Tour en la ciudad de Cáceres

Eventos como World Padel Tour han servido a Extremadura para crecer en materia deportiva, para ampliar nuestra experiencia organizativa en grandes eventos deportivos. Pero también sirvió para apreciar el valor añadido de que todos los visitantes a esa competición disfrutarán y conocieran lo que puede aportarles nuestra región en materias como el turismo, la historia o la gastronomía. Los datos utilizados para la confección de este Informe fueron recogidos por los grupos de investigación Reficaturem y Emturin 2020.Events such as World Padel Tour have served Extremadura to grow in sports, to expand our organizational experience in major sporting events. But it also served to appreciate the added value that all visitors to that competition will enjoy and know what our region can contribute in matters such as tourism, history or gastronomy. The data used for the preparation of this Report were collected by the research groups Reficaturem and Emturin 2020

Situación y evolución de la economía circular en España

Primera entrega de este informe bienal analiza la evolución y la situación actual de la economía circular en España respecto a Europa. El informe coordinado por la Cátedra UNESCO de Sostenibilidad de la Universidad Politécnica de Catalunya (UPC) en colaboración con la Asociación para la Sostenibilidad y el Progreso de las Sociedades, (ASYPS) propone la creación de una hoja de ruta que incluya en primer lugar un sistema integrado de evaluación e indicadores de seguimiento para medir y comparar la circularidad de las economías, y aporta una propuesta de de indicadores que en su conjunto conforman un sistema de evaluación en el que se integran las variables centrales y más relevantes para el análisis, evaluación y seguimiento de la economía circular.En este trabajo se recoge y analizan los indicadores disponibles sobre el nuevo modelo productivo denominado economía circular, que supone un cambio radical en los sistemas de producción y consumo actuales. Se trata del primer informe a nivel nacional sobre La situación y evaluación de la economía circular en España. La economía circular ofrece una alternativa al actual modelo de producción, basado en una cadena de valor lineal que genera residuos en todas las etapas, desde la extracción de materias primas hasta la generación de residuos, pasando por las fases de fabricación, distribución y consumo. La alternativa consiste en prolongar la vida económica útil de los materiales y los recursos tanto como sea posible, reduciendo al mínimo la generación de residuos.Objectius de Desenvolupament Sostenible::6 - Aigua Neta i SanejamentObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaObjectius de Desenvolupament Sostenible::10 - Reducció de les DesigualtatsObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPreprin

Smart facemask for wireless CO2 monitoring

This study was funded by Spanish MCIN/AEI/10.13039/501100011033/ (Projects PID2019-103938RB-I00 and ECQ2018-004937-P) and Junta de Andalucía (Projects B-FQM-243-UGR18, P18-RT-2961 and postdoctoral grant of PE DOC_00520). The projects were partially supported by European Regional Development Funds (ERDF).Source codes for microcontroller firmware (developed with MPLAB X IDE v5.45) and AndroidTM smartphone application (SmartMask v1.0) are available at an open-access repository (URI: http://hdl.handle.net/10481/71668) under a Creative Commons license.The use of facemasks by the general population is recommended worldwide to prevent the spread of SARS-CoV-2. Despite the evidence in favour of facemasks to reduce community transmission, there is also agreement on the potential adverse effects of their prolonged usage, mainly caused by CO2 rebreathing. Herein we report the development of a sensing platform for gaseous CO2 real-time determination inside FFP2 facemasks. The system con- sists of an opto-chemical sensor combined with a flexible, battery-less, near-field-enabled tag with resolution and limit of detection of 103 and 140 ppm respectively, and sensor lifetime of 8 h, which is comparable with recommended FFP2 facemask usage times. We include a custom smartphone application for wireless powering, data processing, alert management, results displaying and sharing. Through performance tests during daily activity and exercise monitoring, we demonstrate its utility for non-invasive, wearable health assessment and its potential applicability for preclinical research and diagnostics.B-FQM-243-UGR18 Consejeria de Economia, Innovacion, Ciencia y Empleo, Junta de Andalucia (Ministry of Economy, Innovation, Science and Employment, Government of Andalucia)P18-RT-2961 Consejeria de Economia, Innovacion, Ciencia y Empleo, Junta de Andalucia (Ministry of Economy, Innovation, Science and Employment, Government of Andalucia)DOC_00520 Consejeria de Economia, Innovacion, Ciencia y Empleo, Junta de Andalucia (Ministry of Economy, Innovation, Science and Employment, Government of Andalucia

Situación y evolución de la economía circular en España : informe 2019

Segunda entrega de este informe bienal. La Economía Circular es una de las grandes transiciones en las que Cotec está inmersa y una de sus líneas prioritarias en las que está centrando sus esfuerzos. Con este segundo informe, se quiere mantener su contribución al análisis de la situación de la Economía Circular en España y proponer una hoja de ruta en la que se recogen prioridades necesarias y estratégicas para la transición de un modelo económico lineal a otro circular, además recoge y analiza los indicadores disponibles hasta la fecha. La economía circular ofrece una alternativa al actual modelo de producción, basado en una cadena de valor lineal que genera residuos en todas las etapas, desde la extracción de materias primas hasta la generación de residuos, pasando por las fases de fabricación, distribución y consumo. La alternativa consiste en prolongar la vida económica útil de los materiales y los recursos tanto como sea posible, reduciendo al mínimo la generación de residuos.Objectius de Desenvolupament Sostenible::6 - Aigua Neta i SanejamentObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaPreprin