CASE STUDIES

Production of ISS/ISE package

PHASTAR was contracted to assist a global pharmaceutical company with their integrated summary of safety and efficacy for a regulatory submission. The work started with assisting the sponsor in defining an integration strategy for approximately 20 phase I studies, six phase II studies and their corresponding extension as well as three phase III studies and their long-term follow-up study. It involved defining a set of relevant analyses sets and reporting periods for both safety and efficacy analyses, ahead of writing up the analysis plans for the phase II pooled analyses, and phase II & III pooled analyses.

Integrated summary of safety

PHASTAR was contracted to assist a pharmaceutical company with the conversion of 16 Legacy studies to SDTM (2 SDTM “Like” phase II studies, 14 Legacy Phase I studies) and with the pooling and reporting activities of their Integrated Summary of Safety (ISS) for a regulatory submission. PHASTAR were supplied with legacy datasets of the 16 Phase I/II studies, Raw CRF, protocol and CSRs, alongside ISS SAP and Shells.

CRO rescue

PHASTAR began a relationship with a top ten pharmaceutical company carrying out statistical consultancy and were subsequently asked to bid to be a partner in a traditional outsourcing model (i.e. the reporting of clinical trials is done independently by PHASTAR using PHASTAR systems and processes). An RFI/RFP process was undertaken, and we responded, giving details of how we would deliver the results of the clinical trials and related deliverables. We were then selected to be one of two strategic partners after agreeing processes, pricing and governance structure. A two-layer governance structure was established, with a quarterly executive review.

Functional service provision

PHASTAR has a number of sponsor relationships where we provide teams of statisticians and statistical programmers to complement the internal teams within medium and large pharma companies. For example, we provide a large integrated production team of statisticians and statistical programmers to a large global pharmaceutical company. The PHASTAR staff primarily work from our offices supporting this company’s development projects across the full range of phase I to IV clinical trials. This PHASTAR integrated production team includes both management and technical oversight. A structured Governance process is used to provide oversight of the integration of the PHASTAR and sponsor teams.

Data management oversight

PHASTAR was engaged to conduct oversight of data management activities on behalf of one of our established clients, a large pharmaceutical company. After acquisition of a biotechnology company our client requested our support to manage two ongoing pharmacokinetic studies in oncology and rheumatology.

Phase I/II oncology study

PHASTAR was engaged to report a phase I/II oncology study in advanced NSCLC with an initial phase I escalation followed by a phase II expansion. The objectives covered defining the maximum tolerated dose and having a preliminary assessment of efficacy using RECIST 1.1 as well as PK/PD endpoints. For this study, PHASTAR was using the sponsor’s system – a UNIX based platform with a web-based front end to manage the SAS programming. The sponsor had dataset standards in place and we were required to have all non-standard variables approved by the sponsor’s standards committee. There were over 100 new variables added to the sponsor’s standards in this study. All dataset programming was validated using Pinnacle 21 tools.

Large respiratory CV outcomes study

Blinded outputs were programmed internally by a sponsor and we had an independent statistician who attended the meetings and gave statistical advice. The sponsor created blinded outputs outside of their standard reporting environment so that we could easily recreate outputs using a standalone SAS environment. We were supplied the DMC charter for review prior to finalization.

Sample size for a sepsis diagnostic study

Recently PHASTAR aided on design input and sample size calculations for a study using a new diagnostic test to detect sepsis in blood samples. For patients presenting with suspected symptoms of sepsis, the current method to confirm the diagnosis is via blood cultures, which takes between 36 - 48 hours to process. Due to the severity and rapid escalation of sepsis, treatment needs to be initiated immediately. Since it is not possible to wait until a result is obtained, the patient is treated for sepsis empirically. There are 25 different bacteria types that can cause sepsis and the treatment depends on the bacteria type. The current test method via blood cultures cannot determine the bacteria type of infection, so the patient is treated for the most common sepsis-causing bacteria type (E. Coli). The new diagnostic test seeks to address both issues. It can return a sepsis diagnosis within 3 - 4 hours while identifying the individual bacteria type.

Rare disease reporting and regulatory support

PHASTAR provided a team of statisticians and programmers to deliver the analysis and reporting of a program of four trials in a genetic rare disease, and the corresponding integrated summary of efficacy and safety, to support a regulatory submission to the FDA. There were four studies comprising twelve, one, two, and three patients. The one and two patient studies were pilot studies that included historical data from hospital notes that had been transcribed into a new clinical trials database (after obtaining informed consent from the patients). The three patient study was a compassionate use study that had been initiated due to patient requests.

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Epilepsy

PHASTAR helped to design and analyse a placebo-controlled crossover trial using an anti-epileptic to assess cognitive function in children. The study had a large number of outcome measures, covering cognitive function but also seizure frequency, an ambulatory EEG assessment, IQ and a behavioural assessment using the Conners Rating Scales. The study was also attempting to determine the relationship between transient ischemic attacks and short-term impacts on cognitive function.

Dose escalation study

A PHASTAR statistician was tasked to assist with the design of a modular protocol to assess safety, tolerability and pharmacokinetics in a novel oncology compound in an acute indication. The first module consisted of a dose escalation portion with the aim of defining the maximum tolerated dose (MTD). An ongoing first-in-human study in a more robust population was using the Continual Reassessment Method (CRM) to determine the MTD, but due to expected early adverse reactions in the more acute population, each dose cycle would need to involve intra-patient titration. With the titration proposed, it was not possible to fit a dose toxicity curve since the actual dose received within each dose level would be variable between patients. For this reason, CRM was rejected, and a keyboard, or modified toxicity probability interval (mTPI-2) design (Guo et al 2017) was proposed by PHASTAR, which would allow the dose levels to follow an ordinal framework and provide better operating characteristics and specificity than a traditional 3+3 or Rolling-six design.

IDAC support

PHASTAR was awarded as a preferred supplier to a top 20 pharmaceutical sponsor, supporting five new studies per year. Each study was several years in length, with IDMC meetings taking place every 3-6 months. PHASTAR worked with this pharmaceutical sponsor through two models for IDAC support; model one offering independent programming of the blinded and unblinded IDMC package, with model two utilising sponsor programming code to produce blinded and unblinded IDMC package.

Preferred partnerships

Phase III asthma study

PHASTAR was engaged to rescue another CRO’s reporting of a large phase III study in mild asthmatics to evaluate the efficacy and safety of the experimental compound. With a primary objective to prove superiority in asthma control, analyses consisted of a complex primary endpoint and a broad range of secondary endpoints which are typically observed in respiratory.

Bayesian network meta-analysis

Hepatitis B virus (HBV) infection is among the most common persistent viral infections in humans. Chronic infection of HBV (CHB) can lead to serious medical complications such as cirrhosis, hepatocellular carcinoma, and liver failure. Surrogate markers are often used to monitor the progression of the disease and patients are treated with antiviral agents to reduce the risk of developing complications.

Investigation into failed bioequivalence study

A new formulation under investigation was planned to be submitted for use in both the US and Europe. As the reference formulation in the US differs from that in Europe, two separate bioequivalence studies were needed to gain approval from the FDA and EMA. Study 1 was conducted for FDA approval and successfully demonstrated bioequivalence. Study 2 was conducted for EMA approval and whilst AUC was within the bioequivalence limits, Cmax was not, and bioequivalence could not formally be declared.

Event prediction to guide clinical study design

Following the completion of an early phase clinical trial, the sponsor was planning a larger phase IIb study with the same compound but wanted to understand whether there were any biomarkers in the clinical data from the first study that were predictive of a specific event.

Missing data in COVID adverse events

Much of the discussion around adverse events (AEs) and serious adverse events (SAEs) in clinical trials through the COVID-19 pandemic has been focussed AEs associated with new therapies and vaccines for COVID-19 itself. But what about other clinical trials that have been impacted by COVID-19? We have already explored the many ways in which the COVID-19 pandemic has affected the conduct, analysis, and interpretation of clinical trials with respect to efficacy data. We have discussed how global quarantines and travel restrictions have resulted in site closures and interruptions to investigational product supply, which have subsequently resulted in issues such as missed assessments and treatment interruptions. But how might these issues also affect safety data?

Central clinical monitoring plan

An integral part of running a Clinical Trial is to monitor and review the accumulated data in order to follow the progress of several important components like patients, sites, and the study endpoints. These in turn break down to measurable performance such as site compliance, patient outliers, treatment schedule in accordance with protocol, predefined endpoints, recruitment progression, and interim data cut-off reporting for the authorities. This monitoring process is described in the Central Clinical Monitoring Plan (CCMP), which outlines all needed outputs required for the study team to be able to successfully monitor the progress of the study.

Patient recruitment forecasting

Many trials tend to not make it to the planned Last Patient, First Treatment date set by trial managers. The client wanted a forecasting algorithm to predict a finishing date for a trial and how many patients they would end up with on the original planned Last Patient, First Treatment date.

Medical monitoring

The customer had previously performed all their clinical trial monitoring using Excel as a tool, requiring extensive manual and labour-intensive processes. For a high-profile study, the customer had implemented a new Electronic Data Capture system (EDC), but this system had no automatic data extraction module. The customer was therefore facing challenges managing and analyzing their data on a regular basis.

Cyber security posture report

This project was a collaboration between Kopenhagen Konsulting and Phastar Denmark. KK Management Konsulting lead the project and report/metric definitions. Phastar Denmark developed and implemented the technical solution. They wanted to automate the process of pulling data reports from multiple systems, calculate various metrics, and create automated visualizations of the figures on a monthly basis.

Event adjudication

The client requested a dynamically updated app with dashboards, event flow overviews, adjudication details and metrics. The app required multiple source input, from both SAS data files and Excel files, across several studies. In addition, the client needed specific metrics endpoints for selected studies.

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