Use Of Model-based Design Research Articles

Integrating model-based design of experiments and computer-aided solvent design

Computer-aided molecular design (CAMD) methods can be used to generate promising solvents with enhanced reaction kinetics, given a reliable model of solvent effects on reaction rates. Herein, we use a surrogate model parameterised from computer experiments, more specifically, quantum-mechanical (QM) data on rate constants. The choice of solvents in which these computer experiments are performed is critical, considering the cost and difficulty of these QM calculations. We investigate the use of model-based design of experiments (MBDoE) to identify an information-rich solvent set and integrate this within a QM-CAMD framework. We find it beneficial to consider a wide range of solvents in designing the solvent set, using group contribution techniques to predict missing solvent properties. We demonstrate, via three case studies, that the use of MBDoE yields surrogate models with good statistics and leads to the identification of solvents with enhanced predicted performance with few iterations and at low computational cost.

Embracing model-based designs for dose-finding trials

Background:Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM).Methods:We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation.Results:We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome.Conclusions:There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia.

The performance of model-based versus rule-based phase I clinical trials in oncology : A quantitative comparison of the performance of model-based versus rule-based phase I trials with molecularly targeted anticancer drugs over the last 2years.

Phase I studies with anticancer drugs are used to evaluate safety and tolerability and to choose a recommended phase II dose (RP2D). Traditionally, phase I trial designs are rule-based, but for several years there is a trend towards model-based designs. Simulations have shown that model-based designs perform better, faster and are safer to establish the RP2D than rule-based designs. However, the superiority of model-based designs has never been confirmed based on true trial performance in practice. To aid evidence-based decisions for designing phase I trials, we compared publications of model-based and rule-based phase I trials in oncology. We reviewed 172 trials that have been published in the last 2years and assessed the following operating characteristics: efficiency (trial duration, population size, dose-levels), patient safety (dose-limiting toxicities (DLTs)) and treatment optimality (percentage of patients treated below and at or above the recommended phase 2 dose). Our results showed a non-significant but clinically relevant difference in trial duration. Model-based trials needed 10months less than rule-based trials (26 versus 36months; p=0.25). Additionally, fewer patients were treated at dose-levels below the RP2D (31% versus 40%; p=0.73) while safety was preserved (13% DLTs versus 14% DLTs). In this review, we provide evidence to encourage the use of model-based designs for future phase I studies, based on a median of 10months of time gain, acceptable toxicity rates and minimization of suboptimal treatment.

AN INDUSTRIAL EMBEDDED CONTROL SYSTEM DESIGN PROCESS

Embedded software provides a flexible and powerful means to differentiate products and achieve competitive advantage. However, the design of embedded software tends to be difficult to manage. This paper describes the use of model-based design to help manage the design of embedded control systems. It presents typical design configurations, such as rapid prototyping, software-in-the-loop, processor-in-the-loop, and hardware-in-the-loop, and discusses their differences and usages. Finally, an overview of some of the aspects typical in developing embedded control systems using model-based design is given.

An Integrated Diagnostic Development Process for Automotive Engine Control Systems

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Theory and applications of model-based fault diagnosis have progressed significantly in the last four decades. In addition, there has been increased use of model-based design and testing in the automotive industry to reduce design errors, perform rapid prototyping, and hardware-in-the-loop simulation (HILS). This paper presents a new model-based diagnostic development process for automotive engine control systems. This process seamlessly employs a graph-based dependency model and mathematical models for online/offline diagnosis. The hybrid method improves the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on empirical models, enables remote diagnosis, and responds to the challenges of increased system complexity. The development platform consists of an engine electronic control unit (ECU) rapid prototyping system and HILS equipment—the air intake subsystem (AIS). The diagnostic strategy is tested and validated using the HILS platform. </para>

Model misbehaviour

Model-based design can be big time-saver in developing complex systems. But, for safety-critical applications, it might be saving too much time - and in all the wrong places. Model-based design techniques are now firmly established in embedded design: particularly at the systems engineering level. From their roots in mathematical and algorithmic modeling, they have moved into the mainstream of product design, valued for their contribution to efforts to achieve results quickly - and reduce lead times and development costs. But the use of model-based design in dependable and mission critical systems raises a whole new raft of questions, particularly when it is combined with automatic code generation.