Design of experiments and manufacturing design space for multi‐step processes

Most industrial processes are composed of multiple subsequent steps. In this article, we provide a statistical approach to design experiments and to define the manufacturing design space of multi-step processes by taking into account the complex system of interactions among steps. We consider each intermediate outcome as an additional input factor in the next step and we plan experiments following a particular sequential structure. To encompass the potential deviations from the target levels of such input factors, designs are selected according to the D-optimality in average criterion and, in order to assess their prediction capabilities, a suitable extension of the fraction of design space technique has been proposed. The manufacturing design space of the process is then defined by combining the interconnected manufacturing design spaces of the process steps and by deriving the linear combination of the process inputs that ensures the required quality standard for the final outcome. Appealing properties of this approach are also shown by the application to a three-steps biochemical process of expression and purification of a recombinant protein in which 10 input factors are included in the design

New insights into adaptive enrichment designs

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns

Design of experiment in drug development: optimal allocations in multi-arm clinical trials and design space for multi-step processes

In this thesis, we deal with the design of experiments in the drug development process, focusing on the design of clinical trials for treatment comparisons (Part I) and the design of preclinical laboratory experiments for proteins development and manufacturing (Part II). In Part I we propose a multi-purpose design methodology for sequential clinical trials. We derived optimal allocations of patients to treatments for testing the efficacy of several experimental groups by also taking into account ethical considerations. We first consider exponential responses for survival trials and we then present a unified framework for heteroscedastic experimental groups that encompasses the general ANOVA set-up. The very good performance of the suggested optimal allocations, in terms of both inferential and ethical characteristics, are illustrated analytically and through several numerical examples, also performing comparisons with other designs proposed in the literature. Part II concerns the planning of experiments for processes composed of multiple steps in the context of preclinical drug development and manufacturing. Following the Quality by Design paradigm, the objective of the multi-step design strategy is the definition of the manufacturing design space of the whole process and, as we consider the interactions among the subsequent steps, our proposal ensures the quality and the safety of the final product, by enabling more flexibility and process robustness in the manufacturing

Optimal and ethical designs for hypothesis testing in multi‐arm exponential trials

Multi-arm clinical trials are complex experiments which involve several objectives. The demand for unequal allocations in a multi-treatment context is growing and adaptive designs are being increasingly used in several areas of medical research. For uncensored and censored exponential responses, we propose a constrained optimization approach in order to derive the design maximizing the power of the multivariate test of homogeneity, under a suitable ethical constraint. In the absence of censoring,we obtain a very simple closed-form solution that dominates the balanced design in terms of power and ethics. Our suggestion can also accommodate delayed responses and staggered entries, and can be implemented via response adaptive rules. While other targets proposed in the literature could present an unethical behavior, the suggested optimal allocation is frequently unbalanced by assigning more patients to the best treatment, both in the absence and presence of censoring. We evaluate the operating characteristics of our proposal theoretically and by simulations, also redesigning a real lung cancer trial, showing that the constrained optimal target guarantees very good performances in terms of ethical demands, power and estimation precision. Therefore, it is a valid and useful tool in designing clinical trials, especially oncological trials and clinical experiments for grave and novel infectious diseases, where the ethical concern is of primary importance

The prognostic value of histology in ulcerative colitis in clinical remission with mesalazine

Background: The aim of the study was to compare the prognostic value of histological and endoscopic activity in patients with ulcerative colitis (UC). Methods: Patients in clinical remission for 1 year under treatment with mesalazine underwent a planned colonoscopy with biopsies. Histological activity was scored using the histological activity index (HAI). Endoscopic activity was scored using the Mayo endoscopic subscore (MES). The clinical course was evaluated measuring relapses needing steroids during a follow up of 3 years. Results: A total of 52 patients were enrolled into the study and followed up for 3 years. At baseline 29 patients (55.77%) had no endoscopic lesions, and 17 patients (32.69%) showed no histological alteration. At 3 years of follow up, overall, 26 patients (50%) were still in steroid-free remission. Using univariate logistic regression analysis, both histological (HAI ⩾ 1) and endoscopic activity (MES ⩾ 1) were significantly associated with outcome, showing, respectively, a relapse risk (odds ratio [OR]) 16.4 times higher than histological remission (HAI 0) (96% confidence interval [CI]: 3.2–84.3) and 6.3 times higher with respect to endoscopic remission (MES 0) (96% CI: 1.9–21.3). After multivariate logistic regression analysis, histological activity was the only factor significantly associated with outcome (OR 10.2; 95% CI: 1.7–59.4). Conclusions: Histological activity has the most powerful prognostic value in predicting the need for steroids in patients with UC in stable clinical remission on mesalazine. It could be considered as a target of therapy in UC