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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
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.
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.
This is a comprehensive major reference work for our SpringerReference program covering clinical trials. Although the core of the Work will focus on the design, analysis, and interpretation of scientific data from clinical trials, a broad spectrum of clinical trial application areas will be covered in detail. This is an important time to develop such a Work, as drug safety and efficacy emphasizes the Clinical Trials process. Because of an immense and growing international disease burden, pharmaceutical and biotechnology companies continue to develop new drugs. Clinical trials have also become extremely globalized in the past 15 years, with over 225,000 international trials ongoing at this point in time. Principles in Practice of Clinical Trials is truly an interdisciplinary that will be divided into the following areas: 1) Clinical Trials Basic Perspectives 2) Regulation and Oversight 3) Basic Trial Designs 4) Advanced Trial Designs 5) Analysis 6) Trial Publication 7) Topics Related Specific Populations and Legal Aspects of Clinical Trials The Work is designed to be comprised of 175 chapters and approximately 2500 pages. The Work will be oriented like many of our SpringerReference Handbooks, presenting detailed and comprehensive expository chapters on broad subjects. The Editors are major figures in the field of clinical trials, and both have written textbooks on the topic. There will also be a slate of 7-8 renowned associate editors that will edit individual sections of the Reference.
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.
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.
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.
The use of modeling and simulation tools is rapidly gaining prominence in the pharmaceutical industry covering a wide range of applications. This book focuses on modeling and simulation tools as they pertain to drug product manufacturing processes, although similar principles and tools may apply to many other areas. Modeling tools can improve fundamental process understanding and provide valuable insights into the manufacturing processes, which can result in significant process improvements and cost savings. With FDA mandating the use of Quality by Design (QbD) principles during manufacturing, reliable modeling techniques can help to alleviate the costs associated with such efforts, and be used to create in silico formulation and process design space. This book is geared toward detailing modeling techniques that are utilized for the various unit operations during drug product manufacturing. By way of examples that include case studies, various modeling principles are explained for the nonexpert end users. A discussion on the role of modeling in quality risk management for manufacturing and application of modeling for continuous manufacturing and biologics is also included. Explains the commonly used modeling and simulation tools Details the modeling of various unit operations commonly utilized in solid dosage drug product manufacturing Practical examples of the application of modeling tools through case studies Discussion of modeling techniques used for a risk-based approach to regulatory filings Explores the usage of modeling in upcoming areas such as continuous manufacturing and biologics manufacturingBullet points