Evaluation of six months sputum culture conversion as a surrogate endpoint in a multidrug resistant-tuberculosis trial

The emergence of multidrug resistant-tuberculosis (MDR-TB), defined as Mycobacterium tuberculosis strains with in vitro resistance to at least isoniazid and rifampicin, has necessitated evaluation and validation of appropriate surrogate endpoints for treatment response in drug trials for MDR-TB. The trial that has demonstrated efficacy of bedaquiline, a diarylquinoline that inhibits mycobacterial ATP synthase, possesses the requisite features to conduct this evaluation. Approval of bedaquiline for use in MDR-TB was based primarily on the results of the controlled C208 Stage II study (ClinicalTrials.gov number, NCT00449644) including 160 patients randomized 1:1 to receive bedaquiline or placebo for 24 weeks when added to an 18-24-month preferred five-drug background regimen. Since randomization in C208 Stage II was preserved until study end, the trial results allow for the investigation of the complex relationship between sustained durable outcome with either Week 8 or Week 24 culture conversion as putative surrogate endpoints. The relationship between Week 120 outcome with Week 8 or Week 24 culture conversion was investigated using a descriptive analysis and with a recently developed statistical methodology for surrogate endpoint evaluation using methods of causal inference. The results demonstrate that sputum culture conversion at 24 weeks is more reliable than sputum culture conversion at 8 weeks when assessing the outcome of adding one new drug to a MDR-TB regimen.

Regulatory landscapes for biomarkers and diagnostic tests: Qualification, approval, and role in clinical practice

While the term 'biomarker' is relatively new, the concept is millennia old. However, with the introduction of new technologies to discover potential biomarkers comes the need to assess their utility and veracity for any given use. This is particularly true for the use of biomarkers to support regulatory decisions in medical product development. Hence the US Food and Drug Administration has developed processes for the qualification of biomarkers and other medical product development tools, processes that are underscored by recent legislation (i.e. the 21st Century Cures Act). In addition to these qualification processes, diagnostic tests that measure a biomarker may follow a process for regulatory decision through the processes that evaluate companion diagnostics. This mini-review provides an overview of these processes and their role in pharmaceutical development and clinical use. Impact statement This work summarizes very recent developments in the US FDA's biomarker qualification program. Furthermore, it contrasts biomarker qualification with companion diagnostic evaluation. As such, it will be highly informative for researches considering taking a biomarker discovery farther along the road to validation.

A new modeling approach for quantifying expert opinion in the drug discovery process

Expert opinion plays an important role when choosing clusters of chemical compounds for further investigation. Often, the process by which the clusters are assigned to the experts for evaluation, the so-called selection process, and the qualitative ratings given by the experts to the clusters (chosen/not chosen) need to be jointly modeled to avoid bias. This approach is referred to as the joint modeling approach. However, misspecifying the selection model may impact the estimation and inferences on parameters in the rating model, which are of most scientific interest. We propose to incorporate the selection process into the analysis by adding a new set of random effects to the rating model and, in this way, avoid the need to model it parametrically. This approach is referred to as the combined model approach. Through simulations, the performance of the combined and joint models was compared in terms of bias and confidence interval coverage. The estimates from the combined model were nearly unbiased, and the derived confidence intervals had coverage probability around 95% in all scenarios considered. In contrast, the estimates from the joint model were severely biased under some form of misspecification of the selection model, and fitting the model was often numerically challenging. The results show that the combined model may offer a safer alternative on which to base inferences when there are doubts about the validity of the selection model. Importantly, thanks to its greater numerical stability, the combined model may outperform the joint model even when the latter is correctly specified.