Quality, reliable data is the key to successful drug development. From the initial preclinical data, the plan to bring a drug to market is built upon the foundation of solid, reliable data that demonstrates safety in a human patient population.

In this journal, we lead you on the complex, multi-step data journey for Phase I clinical trials, from conceptualization and initial protocol development, collection and analysis, through final regulatory submission. We highlight best practices and approaches to mitigate challenges, and show how integration and collaboration build the strongest datasets for your drug development program.

In Issue 29 of The Altascientist: 
•    Protocol development
•    Data management
•    Statistical support
•    Analysis and reporting

Clinical Trial Protocol Development—The First Important Step

Developing a protocol is the first step in making your study design a concrete, actionable plan to generate data for regulatory submission. Meticulously documented and validated, a well-designed protocol provides the structure for the trial activities that will generate the data to support the study objectives; it is integral to the success of any drug development program.

The credibility of the data from the clinical trial is mainly dependent on the trial design. The trial design in the protocol defines the endpoints and study type (double-masked, placebo-controlled, parallel design), and includes detailed information about the investigational product (IP), the anticipated duration of subject participation, and the sequence and duration of all trial periods, including follow-up. Discontinuation criteria are also key elements for data-driven decision-making during study conduct.

In addition, the protocol includes the description of the analysis and statistical methods to be employed, including timing of any planned interim analyses and quantification of the approach, such as selection criteria for analysis populations (all randomized, all dosed, all eligible, etc.), sample size, calculation of power of the trial, and clinical justification. Finally, the protocol details procedures for reporting any deviations from the original statistical plan, and the use of any excess, missing, or spurious data. 

Once the study protocol is final, it becomes the foundation for additional and more detailed study documents, including the Data Management Plan (DMP) and Statistical Analysis Plan (SAP). Here, the first steps of the data journey truly begin.

How Altasciences Will Support Your Clinical Trial Data

At Altasciences, we take pride in the way we handle your data and facilitate your journey. We have a talented, experienced, and integrated team, whose collaborative efforts ensure that your data is collected and managed with the utmost speed, efficiency, and respect for quality.

Following rigorous processes and standard operating procedures and leveraging a deep and broad knowledge of CDASH and CDISC guidelines, we ensure that your critical trial data is delivered in a regulatory-compliant format. Our PK and PD experts ensure that all analyses are carried out with rigor in a timely fashion so that the data is thorough, accurate, and actionable. When you partner with us, you need never be concerned about the quality, security, or accuracy of the data you present in support of your drug development program.
 

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The Clinical Data Interchange Standards Consortium (CDISC) is a worldwide organization for data standardization, ensuring that drug research data delivers the maximum value for sponsors, regulatory agencies, and patients. Data that is accessible, compatible, comparable across regions, and reusable for meta-analysis or reanalysis, serves to improve our understanding of human therapeutics by providing meaningful, efficient research data for the entire global drug research community. Implementing standards to collect, structure, and analyze data makes it easier to aggregate information and take advantage of big data.

In Issue 26 of The Altascientist: 
•    Introduction to standardization and its benefits
•    Client considerations for nonclinical and clinical data standardization
•    Case Study — Realized Efficiency
•    Case Studies — Legacy Data Conversion
•    The future of CDISC and data strategy

Why is Data Standardization in Drug Development Important?

There is tremendous value in standardizing and sharing data: organizations across the globe that are using CDISC standards produce faster, more efficient research, and pave the way for more breakthroughs that amplify the power of data, in both the short- and long-term.

“Adherence to data standards is integral to successful and efficient drug development for our clients,” says Nicole Maciolek, Vice President, Research Services, Medical Writing and Scientific Affairs, at Altasciences. “We are experts in applying CDISC standards, from preclinical SEND to clinical CDASH, SDTM, and ADaM. We are proud to be Gold members of the CDISC consortium, and to bring the advantages of standardization to all the data we deliver.”

Data that is presented in a well-organized, templated fashion is easier for regulatory reviewers to understand and interpret, which limits requests to sponsors for clarification or resubmission. The CDISC standards cover all types of data, from nonclinical to Phase III, across therapeutic areas, and are required for all studies supporting marketing authorization.

Sponsors benefit from data that is of high quality, easy to interpret, and leads to sound, swift go/no-go decisions. Regulatory bodies benefit from a smooth, consistent process that allows them to analyze all the data submitted in the same way, without concerns about the organization or interpretation of data that may be structured differently.

How Altasciences Ensures Compliance With CDSIC Data Standards

We ensure that our team is always up to date with the latest trends and guidelines in regard to CDISC standards. We utilize a suite of proprietary validation checks, as well as Pinnacle 21 Enterprise version technology, to load, review, and validate SDTM and ADaM data and Define.xml files. Our CDISC experts are available on a per-project or full-time equivalent (FTE) basis, according to your needs. Our team has converted data from clinical trials, in diverse therapeutic areas, to CDISC-compliant SDTM and ADaM datasets, as well as designed CDASH-compliant forms and databases for streamlined data management and reporting.

Click here to learn more about our clinical data management capabilities.  

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The quality of study data depends on a rigorous sample management process that safeguards the integrity of samples at every step, from initial specimen collection through bioanalysis, to post-study storage. A CRO partner that conducts both preclinical and clinical studies, and supporting bioanalysis, ensures continuity of processes, knowledge sharing, and a centralized location for long-term storage of all your samples.

Issue 23 of The Altascientist provides a deep dive into: 

• sample collection and storage conditions
• standardized workflows for risk Management and chain of custody
• stability testing
• laboratory management
• customized processes
• management of bioanalytical matrices
• post-study sample management

Why Does Sample Management Matter? 

The proper handling of specimens (obtained from the trial participant) and samples (prepared for lab analysis), and their precise, timely treatment is crucial for reliable, robust data, and good decision making. When making a go/no-go decision for a new therapeutic, there must be certainty that the data which informs the decision is of the highest accuracy. 

At both the bioanalytical site and the clinical site, meticulous workflows for safeguarding samples must be in place. Careful recording and storage of the samples, according to the study protocol and all relevant regulations and guidelines (e.g., GLP, GCP, ICH), is integral to the successful collection and analysis of trial data. Well-trained staff, managed by a dedicated bioanalytical Principal Investigator with a clear mandate to safeguard the integrity of the data, is a valuable asset.

Sample Collection and Storage Conditions