Author : Mark Chang
Publisher : CRC Press
Page : 566 pages
File Size : 28,97 MB
Release : 2010-09-29
Category : Mathematics
ISBN : 1439835934
Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and metho
Author : David Ivan Sobell
Publisher :
Page : 172 pages
File Size : 11,86 MB
Release : 1991
Category :
ISBN :
Author : Lukas Eberle
Publisher : Elsevier Inc. Chapters
Page : 16 pages
File Size : 39,69 MB
Release : 2013-06-10
Category : Science
ISBN : 0128086130
Reliable product supply is one of the most critical missions of the pharmaceutical industry. The lead time, i.e. the duration between start and end of an activity, needs to be managed in any production facility in order to make scheduling predictable' agile and flexible. We present a method for measuring and improving production lead time of pharmaceutical processes with a primary focus on Parenterals (i.e. injectables) production processes. Monte Carlo simulation (MCS) is applied for quantifying the total lead time (TLT) of a batch production as a probability distribution and sensitivity analysis reveals the ranking of sub-processes by impact on TLT. Based on these results, what-if analyses are performed to evaluate effects of investments, resource allocations and process improvements on TLT. An industrial case study was performed at a production site for Parenterals of F. Hoffmann-La Roche in Kaiseraugst, Switzerland, where the presented method supported analysis and decision-making of production enhancements.
Author : Donald Dibra
Publisher : BoD – Books on Demand
Page : 110 pages
File Size : 29,31 MB
Release : 2015-04-28
Category : Business & Economics
ISBN : 3734755433
This work presents the application of the Monte Carlo Simulation method and the Decision Tree Analysis approach when dealing with the economic valuation of projects which are subjected to risks and uncertainties. The Net Present Value of a project is usually used as an investment decision parameter. Using deterministic models to calculate a project’s Net Present Value neglects the risky and uncertain nature of real life projects and consequently leads to useless valuation results. Realistic valuation models need to use probability density distributions for the input parameters and certain probabilities for the occurrence of specific events during the life time of a project in combination with the Monte Carlo Simulation method and the Decision Tree Analysis approach. After a short introduction a brief explanation of the traditional project valuation methods is given. The main focus of this work lies in using the Net Present Value method as a basic valuation tool in conjunction with the Monte Carlo Simulation technique and the Decision Tree Analysis approach to form a comprehensive method for project valuation under risk and uncertainty. The extensive project valuation methodology introduced is applied on two fictional projects, one from the pharmaceutical sector and one from the oil and gas exploration and production industry. Both industries deal with high risks, high uncertainties and high costs, but also high rewards. The example from the pharmaceutical industry illustrates very well how the application of the Monte Carlo Simulation and Decision Tree Analysis method, results in a well-diversified portfolio of new drugs with the highest reward at minimum possible risk. Applying the presented probabilistic project valuation approach on the oil exploration and production project shows how to reduce the risk of losing big.
Author : Pooneh Saidi Bidokhti
Publisher : BoD – Books on Demand
Page : 189 pages
File Size : 33,27 MB
Release : 2019-12-18
Category : Computers
ISBN : 1789855454
The Monte Carlo method is a numerical technique to model the probability of all possible outcomes in a process that cannot easily be predicted due to the interference of random variables. It is a technique used to understand the impact of risk, uncertainty, and ambiguity in forecasting models. However, this technique is complicated by the amount of computer time required to achieve sufficient precision in the simulations and evaluate their accuracy. This book discusses the general principles of the Monte Carlo method with an emphasis on techniques to decrease simulation time and increase accuracy.
Author : Vera Trautwein
Publisher :
Page : pages
File Size : 39,74 MB
Release : 2016
Category :
ISBN :
In this thesis an attempt is made to present a framework for designing and improving pharmaceutical manufacturing processes based on a methodology that integrates quantitative risk management. Conducting an in-depth case study at a pharmaceutical manufacturer allows for the development of new theory regarding potential trade-offs between process design objectives. Industry practitioners and previous research studies have focused on flexibility, throughput time, efficiency, automation and quality as main objectives during process improvement efforts. Among those five variables, product quality is the main operational risk and mostly assessed qualitatively. This thesis contributes to the existing literature by introducing a more quantitative approach to risk assessment in the pharmaceutical industry. The proposed model relies on a Monte Carlo simulation to determine the quality loss distribution and assess the distributional tail through the principles of Extreme Value Theory. When combined with the other process objectives, the quantification of quality risk leads to novel insights into the trade-offs faced by pharmaceutical manufacturers during process design decisions. In particular, the findings are synthesized by describing process designs through their performance on these identified objectives. Furthermore, products are grouped by distinctive characteristics and then matched to their ideal process designs; an ideal product-process combination is one which exploits reinforcing relationships amongst process objectives and avoids trade-offs between them. The general framework resulting out of these matches places quality risk at the center of attention for future process design and improvement initiatives in the pharmaceutical industry.
Author : Jeffrey Brian Heyman
Publisher :
Page : 106 pages
File Size : 13,21 MB
Release : 2010
Category :
ISBN :
With high costs and growing concern about research and development (R&D) productivity, the pharmaceutical industry is under pressure to efficiently allocate R&D funds. Nonetheless, pharmaceutical R&D involves considerable uncertainty, including high project attrition, high project-to-project variability in required time and resources, and long time for a project to progress from a biological concept to commercial drug. Despite this uncertainty, senior leaders must make decisions today about R&D portfolio size and balance, the impact of which will not be observable for many years. This thesis investigates the effectiveness of simulation modeling to add clarity in this uncertain environment. Specifically, performing research at Novartis Institutes for Biomedical Research, we aim to design a process for developing a portfolio forecasting model, develop the model itself, and evaluate its utility in aiding R&D portfolio decision-making. The model will serve as a tool to bridge strategy and execution by anticipating whether future goals for drug pipeline throughput are likely to be achievable given the current project portfolio, or whether adjustments to the portfolio are warranted. The modeling process has successfully delivered a pipeline model that outputs probabilistic forecasts of key portfolio metrics, including portfolio size, positive clinical readouts, and research phase transitions. The model utilizes historical data to construct probability distributions to stochastically represent key input parameters, and Monte Carlo simulation to capture the uncertainty of these parameters in pipeline forecasts. Model validation shows good accuracy for aggregate metrics, and preliminary user feedback suggests strong initial buy-in within the organization.
Author : Richard L. Morin
Publisher : Springer
Page : 256 pages
File Size : 15,91 MB
Release : 1988-07-31
Category : Mathematics
ISBN :
Author : Emily Rose Edwards
Publisher :
Page : 52 pages
File Size : 41,57 MB
Release : 2011
Category :
ISBN :
This thesis investigates the feasibility of using a complex model with a Monte Carlo simulation model to forecast the financial, personnel, and manufacturing capacity resources needed for biologic drug development. Accurate forecasting is integral across industries in order to make strong longterm, strategic decisions and an area many companies struggle with. The resources required for the development of a biologic drug are especially hard to estimate due to the variability in the time and probability of success of each development phase. However, in the pharmaceutical industry getting products to market faster allows the company more time to recoup the substantial development investments before the patent expires and also potentially has a large impact on a company's market share. For these reasons, Novartis Biologics wanted to develop a simulation model to provide an objective opinion and assist them in their long-range planning. This thesis describes the design, development, and functionalities of the resultant model. During validation runs, the model demonstrated accuracy of greater than 90% when compared against historical data for headcount, number of campaigns, costs, and projects per year. In addition, the model contains Monte Carlo simulation capabilities to allow users to forecast variability and test the sensitivity of the results. This proves the model can be confidently used by project management, operations, and finance to predict their respective future resource needs.
Author : Francesco L. Gervasio
Publisher : John Wiley & Sons
Page : 420 pages
File Size : 26,49 MB
Release : 2019-01-04
Category : Medical
ISBN : 3527806857
A guide to applying the power of modern simulation tools to better drug design Biomolecular Simulations in Structure-based Drug Discovery offers an up-to-date and comprehensive review of modern simulation tools and their applications in real-life drug discovery, for better and quicker results in structure-based drug design. The authors describe common tools used in the biomolecular simulation of drugs and their targets and offer an analysis of the accuracy of the predictions. They also show how to integrate modeling with other experimental data. Filled with numerous case studies from different therapeutic fields, the book helps professionals to quickly adopt these new methods for their current projects. Experts from the pharmaceutical industry and academic institutions present real-life examples for important target classes such as GPCRs, ion channels and amyloids as well as for common challenges in structure-based drug discovery. Biomolecular Simulations in Structure-based Drug Discovery is an important resource that: -Contains a review of the current generation of biomolecular simulation tools that have the robustness and speed that allows them to be used as routine tools by non-specialists -Includes information on the novel methods and strategies for the modeling of drug-target interactions within the framework of real-life drug discovery and development -Offers numerous illustrative case studies from a wide-range of therapeutic fields -Presents an application-oriented reference that is ideal for those working in the various fields Written for medicinal chemists, professionals in the pharmaceutical industry, and pharmaceutical chemists, Biomolecular Simulations in Structure-based Drug Discovery is a comprehensive resource to modern simulation tools that complement and have the potential to complement or replace laboratory assays for better results in drug design.
