Case Study 1: How bioinformatics analysis and personalised medicine approach was able to salvage a compound.

Scenario

A pharma start-up had invested heavily in the development of a compound using bio-simulation techniques. The related biomarkers were known to be present in several cancer types, based on pre-existing research. The identification of drug targets based on specific genomic biomarkers related to disease progression lead to an immuno-therapeutic compound.

The results of initial animal studies were promising and suggested that the new anti-body based drug would be more effective than the existing in-class alternative currently on the market in the treatment of colorectal (CRC) and non-small cell lung (NSCLC) cancer.

The company had worked with a full-service CRO to conduct a phase I and II study in humans. The phase II results struggled to define an optimal effective dose and a pilot phase III study for efficacy using a crossover-design had not been able to establish efficacy/equivalence in a small patient sample. The company was looking at abandoning the compound but came to us for further advice.

Solution

Upon reviewing the patient data it was noted that roughly half of the patients in their clinical trial responded positively to the treatment while other patients didn’t show a sufficient response. The patients who didn’t respond also seemed to have worse side effects. This affected the overall efficacy of the therapeutic, as determined by the study, as well as it’s side effect profile.

Our biostatistics team decided to analyse the clinical data of patients who showed a clinically meaningful response to the therapeutic against those who didn’t to see if there were any differences between the two groups. After comparing demographics, baseline disease, and other characteristics between the two groups there did not appear to be a compelling difference. It was noted however that responders were slightly more likely to have been in the group that received the novel compound at sage 2 of the study (group 2), rather than stage 1.

Genomic data had also been collected from the patients which we subject to bioinformatic analysis. A few biomarkers of interest were identified which included a mutation in the KRAS pathway, a signalling pathway with a role in several key cell processes, including proliferation. One biomarker in particular was present only in patients with a limited response to the therapeutic.

The company decided to conduct a follow-up study. This study was a biomarker-guided clinical trial, which aimed to compare treatment efficacy of the novel compound vs an existing anti-body based treatment. Subjects were restricted those we had established in the genomic analysis to be likely responders.

A parallel design was used. There were two reasons for this choice. Firstly, by restricting the patient sample to those evidenced as likely to respond, there was no longer the same level of treatment risk that necessitated a cross-over design in the previous study.

Secondly, the fact that in the cross-over study most responders had been clustered in group 2, and that some non-responders in group 1 lacked the biomarker associated with non-response hinted at the possibility of paradoxical progression in some patients taking the novel treatment. If this were the case, it would mean that a cross-over design was not the optimal way to assess efficacy moving forward.

Outcome

This follow-up study showed that the new therapeutic was more effective than the standard treatment. As predicted there was a delayed response in some patients. The company was able to focus on marketing the novel compound to patients without the biomarker using a personalised medicine approach. Significant financial loss was mitigated compared to if the compound would have been abandoned.

Case Study 2: Use of Bayesian adaptive methods in a medical device clinical trial.

Scenario (Stent)

A med-tech company had invested in the research and development of a new polymer material for coronary artery stents. While the newly proposed material for use in stents was promising, the amount of quality data on its use in biological implants was limited. Therefore, extensive safety studies during R&D and clinical trials had to be carried out. At the R&D phase biocompatibility testing data was collected in animals. The device was then tested to ensure minimal endovascular trauma, mechanical stability, and that the material was MRI-safe as well as suitable for fluoroscopic guidance to enable safe implantation into the subject by catheter. Advice was sought as to analysis of the R&D data as well as the design of an initial clinical study to compare their new stents with existing bare metal and drug-eluting stents.

Solution

The biocompatability data from R&D studies was analysed and factors including biotoxicity, haemocompatibility, and the potential for leachables were evaluated against their respective benchmarks.

The company had planned to follow these R&D stages with clinical studies. A pivotal study was designed using Bayesian adaptive methods..

A statistical analysis plan (SAP), sample size report and randomisation schedule were produced for the study and these were then used to inform the study protocol. A parallel groups equivalence design was chosen based on a repeated measures ANOVA design with some adjustments for multiplicity. Two insertion methods were included for each stent type in the study (angiography guided vs without). Some endpoints of the study included the success rate of the initial procedure (e.g. resulting in improved blood flow and a widened artery lumen), restenosis rate, and mean time to restenosis. Kaplan Meier method was used to model time to adverse events and a Cox PH frailty model adjusting for relevant covariates was used for time to restenosis.

The sample size for the clinical trial was planned using Bayesian meta-analysis to derive the parameters necessary for sample size calculation. This included patient clinical data/ literature of stent safety/efficacy for comparable devices. During this process a hierarchical random-effects model using MCMC methods was used to accommodate the heterogeneity of the included studies. Informative prior distributions were used to simulate sample size based on the study design.

An interim analysis at week 12 was planned to assess for differences between the two insertion methods in terms of success rate of the procedure and restenosis rate. This data would be used to for an updated prior for the final analysis. A sample size readjustment using Bayesian methods was to be performed if there was a significantly lower success rate of the initial procedure or a higher rate of restenosis in one insertion method versus the other.

Following the pivotal trial, post-market surveillance studies were planned to monitor device effectiveness and long term biocompatibility compared to bare metal and drug-eluting stents. Adverse events for this were 5 year all cause mortality rate, cardiovascular death, spontaneous MI, procedural MI, stroke, repeat re-vascularisation.

• The benefits of taking a Bayesian approach were:
• The incorporation of external data,
• The use of data from intermediate outcomes in an interim analysis and adjust the design if necessary.
• Better manage any missing endpoint data which was important given a smaller sample size.
• Able to better manage and adjust for multiplicity.

Outcome

The med-tech company would benefit from:

• Device backed by quality evidence that was able to make use of all available data.
• Proven device safety and thereby complies with regulatory requirements.

• Statistical support at each stage of the study, therefore having the best evidence at early stages to prove device safety and use to determine future research intensity.

• The company was able to incorporate a Bayesian adaptive design for device surveillance.