Successfully developing new products and maintaining existing ones are basically data-driven learning processes. The idea is to accumulate data over a period of time and then transform that data into relevant information that supports decisions. The kind of data you include informs the decisions you make, which determines the viability of your products.
To ensure the most-accurate results, you must create the best conditions to generate data—such as optimal clinical trials or careful experiment design-and apply the appropriate kind of statistical analysis.
Of course, doing all of this quickly, efficiently and in compliance with regulators is equally important. This guide is designed to help you navigate the world of statistics and data science so you can identify what you have to do to ensure success.
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Data Science 101
To understand the importance of data science, let’s define the term clearly.
When it comes to medical research and product development, data science refers to a broad, rapidly evolving field that supports the analytical process of extracting knowledge from a variety of data sources and types.
Data scientists develop customized analytical interfaces to evaluate real-world evidence, population databases and proprietary client data.
Integrating different data types and sources—such as genomics, transcriptomics, proteomics, phenomics, imaging and wearables—with customized analytical processes helps you identify the most-meaningful inferences that lead to the best decisions.
It is important to note that data science is based on multiple areas of expertise. By working together, different analytical methods offer a holistic picture that translates into valuable insights and practical steps for your product development, marketing and maintenance strategies.
Statistics 101
Many would say statistics is the cornerstone of all data science. Statistics has very broad applications in the fields of medical research and product development.
Because statistics is one of the most powerful tools in your analytical arsenal, it is important to understand how it can work for you.
As a mathematical discipline, statistics offers a sound methodology for determining optimal designs for experiments, validating analytical methods, evaluating product stability and, ultimately, justifying the use of a particular drug or treatment. In fact, statistics is ubiquitous—from discovery to submission, to manufacturing. Statisticians use probabilistic theory to answer a wide range of questions and drive effective decision making.
And as computing power increases, so do the possibilities. Now you can take advantage of classical statistics under the umbrella of Bayesian statistics to solve your most complex problems.
Analyzing the Product Lifecycle
From discovery to clinical development, to manufacturing
The insights you need during discovery will differ from the analyses you require during clinical trials, clinical development and product development.
Let’s classify how statistics and data science can support each stage of the product lifecycle.
- Guarantee the replicability of experiments
- Support drug target discovery and validations via genomics and transcriptomics
- Ensure reliability of data with optimized assay design.
- Simulate and leverage available data to optimize clinical trials
- Develop biomarker-driven trial design
- Support experimental design within trials
- Analyze relationships between biomarkers and clinical end points in search of predictive, prognostic, disease or monitoring biomarkers
- Stratify patients by different efficacy or safety profiles
Product Development
and Manufacturing
- Improve manufacturing safety and efficiency with a quality-by-design strategy
- Gain in-depth understanding of the process and its potential improvements through new analytical technologies
- Maintain product quality throughout product lifecycle by developing predictive models
The strength of Bayesian and Non-Clinical Statistics
Data is your biggest asset—but only if you know how to interpret it in terms that are relevant to your needs. All the number crunching in the world will not serve you unless it is informed by expert interpretation.
This is where the field of probability plays an important role. Make sure you have access to experts in Bayesian statistics and non-clinical statistics. By applying probability calculations, you can see big-picture interpretations of your data.
It is also critical to bridge the gap between biology and data science by having biologists and bioinformaticians on your team who can offer their scientific interpretations of the numbers.
Glossary of Key Terms
1. Bayesian Statistics
Bayesian statistics is an alternative approach to classical statistics that has gained enormous popularity in the past 20 years. Although classical statistics views unknowns as fixed targets and relies primarily on P values for scientific inference, Bayesian statistics requires that one state a prior distribution that quantifies the analysts’ uncertainty before any data has been observed. Bayesian methods also permit direct probability statements about unknowns, which are easier for non-statisticians to understand.
The approach is ideal for a wide variety of data science applications such as multivariate analysis, longitudinal and survival data, the handling of correlated or missing data, network meta-analysis, predictive modeling, and spatiotemporal modeling.
Even though Bayesian techniques require a lot of computational power, they are now readily implemented via modern software packages like BUGS (Bayesian inference Using Gibbs Sampling) and Stan.
Take a deeper dive into the world of Bayesian statistics.
2. Bioassay Method Validation
Just like physico-chemical procedures, bioassays must be validated to learn how their uncertainty affects the quality of results and any conclusions drawn from those results.
In general, bioassays carry much larger uncertainty given the biological nature of the process needed to get a measurement. However, the objective remains the same: to demonstrate the quality of the obtained results.
Learn more about bioassay method validation.
3. Biomarker Statistics
As defined by the FDA-NIH Biomarker Working Group, a biomarker is “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention, including therapeutic interventions.” Biomarkers play key roles in precision medicine, and the analysis of biomarker data requires the integration of smart analytics and biological knowledge.
For more details on biomarker statistics.
4. Data Visualization
The aphorism “A picture is worth a thousand words” is particularly true when you consider a visual representation of complex or large data. An interactive image can help you see relationships, identify trends or find the needle in the haystack. Flexible visualization tools can look at data from every angle and can be highly effective forms of communication between the analytical team and the scientist or end-user.
For a closer look at data visualization.
5. Machine Learning
Algorithms use statistics to find patterns in abundant, high-quality, potentially complex data of various types, including clinical, genomic, transcriptomic and proteomic data. Machine learning can inform data-driven decision making and can often speed up the process of drug discovery and development.
Gain more insight into machine learning.
6. Physico-Chemical Method Validation
Method validation is defined as the process of demonstrating that analytical procedures are suitable for their intended use.
By validating the physico-chemical methods used from early product development to manufacturing, you determine that the results are of sufficient quality and that uncertainty and biases are at acceptable levels.
The main documents regulating validation are ICH Q2 and ICH Q14. Additional guidance comes from USP <1220> and normative references such as NF EN ISO / IEC 17025, NE EN ISO 15189:2012 and good laboratory practices.
Read more about physico-chemical method validation.
7. Potency Assays
Potency assays measure the ability of a drug to elicit a particular response at a certain dose in a relevant biological system. They are usually required by regulatory agencies for release of drug product under good manufacturing practice but can be used earlier in drug development to characterize intermediates, drug substances and products during process optimization, characterization and performance qualification. They are also useful in drug discovery to rank potential therapeutic candidates, and they are key to understanding the drug mechanisms of actions.
Learn more about potency assays.
8. Precision Medicine
According to the US Precision Medicine Initiative, precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.” Relying on biomarkers and -omics technologies, precision medicine usually involves highly complex data. Smart analytics, computing power and knowledge of genetics and genomics are key to realizing the promise of precision medicine.
Global Reach. Local Presence.
Find your Local Contact
Resources
PharmaLex offer a pool of resources to showcase our experience in the field of Statistics and Data Science. Click below to access some of our most recent valuable content.