José Miguel Laínez

Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry

Abstract Enterprise-wide decision support applications have received increased attention in the chemical process industry in the last decade. In this paper applications, which have real or potential relevance to the pharmaceutical industry, are reviewed. Specific attention is given to the three key phases in the life cycle of an innovative drug product, namely, product development pipeline management, capacity planning and supply chain management. The status of published research in these domains is reviewed, some gaps in the literature are identified and opportunities for further research effort by the process systems engineering community suggested.

Comparison of an assumption‐free Bayesian approach with Optimal Sampling Schedule to a maximum a posteriori Approach for Personalizing Cyclophosphamide Dosing

Variable metabolism, dose‐dependent efficacy, and a narrow therapeutic target of cyclophosphamide (CY) suggest that dosing based on individual pharmacokinetics (PK) will improve efficacy and minimize toxicity. Real‐time individualized CY dose adjustment was previously explored using a maximum a posteriori (MAP) approach based on a five serum–PK sampling in patients with hematologic malignancy undergoing stem cell transplantation. The MAP approach resulted in an improved toxicity profile without sacrificing efficacy. However, extensive PK sampling is costly and not generally applicable in the clinic. We hypothesize that the assumption‐free Bayesian approach (AFBA) can reduce sampling requirements, while improving the accuracy of results.

Validation of a novel approach for dose individualization in pharmacotherapy using gabapentin in a proof of principles study

To demonstrate the premise of individualized dosing charts (IDCs) as a clinical‐bedside decision‐support tool to individualize dosage regimens for drugs in which the interpatient variability is controlled by the pharmacokinetic (PK) behavior of the patient, to calculate the optimal sampling schedule (OSS), which minimizes the number of blood samples per patient. The approach is illustrated with available PK data for gabapentin.

Operational, Tactical and Strategical Integration for Enterprise Decision-Making

Abstract Enterprises are highly complex systems in which one or more organizations share a definite mission, goals and objectives to offer a product or service. As a result, decision-making in the enterprise becomes a highly challenging task, which is usually classified in several levels. In this work, an ontological framework is built as a mechanism for exchanging information and knowledge models for multiple applications and effective integration between the different hierarchical levels. The potential of the general semantic framework developed (model maintenance, usability and re-usability) is demonstrated using a case study concerning the enterprise supply chain network design-planning problem.

Optimal location of gasification plants for electricity production in rural areas

Abstract This work deals with energy supply chains (SCs) based on biomass as a feedstock at small scale using mathematical modeling as a support for decision making. It is recognized that to overcome the main drawbacks of modular gasification systems, technical, financial and institutional issues, an integrated approach embracing all the echelons of the SC is needed. This work sets out the problem as a multi-objective Mixed Integer Linear Program (moMILP) taking into account two main criteria: the net present value (NPV) and the environmental impact through Impact 2002+ indicator. The methodology is applied to a bio-based energy SC with cassava waste as raw material in a rural area from Ghana.

Mathematical Knowledge Management for Enterprise Decision Making

Abstract The basis of decision-making in the enterprise consists of formally representing the system and its subsystems in a model which adequately captures those features which are necessary to reach consistent decisions. This works aims at integrating the elements of the enterprise (i.e., decisions, parameters, constraints, performance indicators) which are included in mathematical models to a semantic representation of the enterprise by the creation of an ontology which captures the meaning of the mathematical language. As a result, the integration of decisions in enterprise may be achieved. The purpose of this work is illustrated in a case study related to plant capacity in the supply chain and scheduling problems.

Integration of Methods for Optimization in a Knowledge Management Framework

Abstract The solution of process systems engineering problems involves their formal representation and application of algorithms and strategies related to several scientific disciplines, such as computer science or operations research. In this work, the domain of operations research is modelled within a semantic representation in order to systematize the application of the available methods and tools to the decision-making processes within organizations. As a result, an operation research ontology is created. Such ontology is embedded in a wider framework that contains two additional ontologies, namely, the enterprise ontology project and a mathematical representation, and additionally it communicates with optimization algorithms. The new ontology provides a means for automating the creation of mathematical models based on operations research principles.

A Variational Bayesian Approach for Dosage Regimen Individualization

Abstract Clinical trials generate a vast amount of data which is rarely exploited for improving therapeutic treatments. A Bayesian approach has been developed by Lainez et al. (2011) for determining individualized dosage regimens. This method combines knowledge of the population of patients (the Prior, which is constructed from the clinical trials data) with measurements from the patient (the Likelihood) to produce a patient specific probability distribution (the Posterior). This posterior is then used to define dosage regimens for which the drug concentration in the blood is kept within the therapeutic window at a given confidence level. Monte Carlo approaches used to perform this analysis can be computationally demanding. Hence, we propose an alternative optimization procedure based on variational Bayesian inference. A computational performance study comparing these two approaches is reported using pharmacokinetic data from Gabapentin, a therapeutic agent for treating convulsive diseases. Since the Bayesian approach has been also used in developing kinetics models for catalytic and polymerization applications, the conclusions of this study may have general relevance beyond the pharmacokinetic domain.