Biostatistics in Pharmaceutical Product Development

Biostatistics in Pharmaceutical Product Development in Industrial Pharmacy

What is Biostatistics?

Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data. In applying statistics to a scientific, industrial, or social problem.

Biostatistics

Biostatistics is the application of statistics in the development and use of therapeutic drugs and devices in humans and animals.

It involve the use of scientific and quantitative procedures in descriptive and inferential statistics to evaluate the quality of evidence in biological sciences. and also involves the statistical processes and methods used for analysing the biological phenomena. It is a science that includes designing of biological and experimental study designs as well as synthesis, analysis, and interpretation of data obtained from such studies.

Applying statistical theories to health and disease problems in the living world is the main focus of biostatistics. Biostatistics in pharmaceutical product development involves various statistical operations, such as designing and conducting biomedical experiments, clinical trials, and development of related computational algorithms.

Biostatistics  forms an important part in epidemiological research, development of health policies, health economics, public health administration, evidence-based practice in clinical medicine, genomics, proteomics, and development of various pharmaceutical products.

• Statisticians often use the method of comparison. We want to know the effect of a treatment (like the Salk vaccine) on a response (like getting polio). Compare the responses of a treatment group with a control group.
• Biostatistics covers applications and contributions not only from health, medicines and, nutrition but also from fields such as genetics, biology, epidemiology, and many others.
• It is mainly consists of various steps like generation of hypothesis, collection of data, and application of statistical analysis.
• Any science needs precision for its development. Precision is all the more important when it comes to health sciences. For precision state facts, observations or measurements in figures.

History

Sir Francis Galton is the Father of Biostatistics. He was the first to apply statistical methods to the study of human differences and inheritance of intelligence, and introduced the use of Questionnaires and Surveys for collecting data on human communities, which he needed for genealogical and biographical works and for his anthropometric studies.

Pharmaceutical Industry

• Revise standard pharmaceutical development practices in experimental design, trial conduct, and statistical analysis to ensure the development and approval of safe, effective pharmaceuticals.
• The last four decades have confirmed the value of prospective, controlled, blinded, randomized clinical trials in pharmaceutical development.
• Refinements statistical of experimental designs analyses, along with and global harmonization of regulatory dossiers, have led to our present status where the basic tenets of phase I to III clinical trials are ubiquitous.

Experimental Design in Clinical Trials

Clinical trials for new drugs, devices, or biological products are conducted in three sequential phases before submitting data to regulatory authorities for approval. Biostatisticians collaborate with medical, regulatory, and data management experts to design each phase of the trial.

Sample Size and Experimental Design: The trial design includes what to measure, how often, subject selection, treatment randomization, and analysis strategies. All factors, including expected results and sample size, are pre-specified in the clinical protocol. Statisticians establish the required sample size, which is critical for Phase III trials to demonstrate statistical significance for marketing approval.

The results from Phase II trials provide evidence to determine the appropriate sample size for Phase III. Factors considered include the expected treatment benefit, primary efficacy parameter, data variability, significance level, and statistical power (the probability of detecting a significant effect if the drug is effective).

Challenges in Predicting Drug Response: It is difficult to accurately predict an individual’s response to a drug due to biological differences, daily life factors, and limited understanding of biology and pharmacology. The unpredictable variations in individual responses and the need to predict population-level effects are reasons for involving biostatisticians in clinical trial design and analysis.
Inferential statistics, a branch of biostatistics, makes inferences about populations by analyzing data from representative samples.

Sensitivity and Cost

Increasing the sample size in a trial will increase the assurance that normal statistical techniques will achieve statistical significance when analyzing the trial results for an effective drug. Sample sizes are always compromised between what is ideal and what is affordable as every sponsor of a new drug has some financial and time limitations. In such case, a biostatistician should define the parameters around this compromise.

Consequently, the sensitivity of a trial to detect the statistically significant efficacy effect for a new drug partially depends on sample size. All other things being equal, larger the sample size, less effective a drug should be to give a significant and potentially approvable result. For marketing approval, the financial investment is one of the important determinants in clinical trials. But, it is often misunderstood that this statistical fact has critical societal implications.

Recommendations: For approval of new drugs, we need a major standard shift in our thinking regarding the statistical gold standards. It would be consistent with sound statistical principles to abandon the rigid and ubiquitous alpha = 0.05 hurdle for regulatory approval. There should be a priority process regarding the establishment of consistent treatment effect with clinically meaningful benefit, along with an agreement to both the acceptable width of a confidence interval around that benefit and the degree of assurance required for that confidence interval. The degree of assurance could be specified for several confidence intervals, designed to show the effect of differences in required precision on the interval width. Establish these parameters based on the already known or expected risks, and also on disease severity and the availability of alternative, effective, and safe therapies.

Random Sampling

The two requirements regarding the random processes include underlying all the inferential statistics and the ability to forecast population benefits from clinical trials. At first, select the subjects for clinical trials and represent the sample of the population of future subjects in the total population that are eligible for drug use. Secondly,assign treatments randomly to clinical trial subjects for comparative trials. Although most of the major trials adhere to the second requirement, a few of them may follow the first.

Recommendations: For a clinical trial, the biostatistician should use the sampling frame to represent the future population. If not possible, use advanced approaches to stratified sampling as Biostatistics is essential in pharmaceutical product development.

For accurate inferences, adjustments should be made during analysis if the relative sizes of strata differ from the population. Though not commonly accepted, for pivotal trials, the statistical community should work to improve trial relevance to the drug’s target population.

Applications

Biostatistics in Pharmaceutical product development has various applications:

• Medicine is essentially an empirical science. It depends on observations and not on theories or theorems.
• As a part of clinical practice or research we deal with many observations, which when systematically arranged, are called Data.
• The process of converting data into information requires a special approach called statistics.
• ‘Statistic’ means a measured or counted fact or piece of the information, stated as a figure such as height of one person, birth weight of a baby etc.

• Statistics will assume a more inter disciplinary form. Use of software other than SAS likely to grow further. In India, innovative statistical methods will help propel the development of biosimilars.
• The future is exciting with statistics being used for real world evidence, development of biosimilars, mining of adverse event data and becoming a core function in medicinal product development and lifecycle maintenance.

Application and uses of Biostatistics

As a science in Physiology

• to define what is normal/healthy in a population
• to find limits of normality
• to find difference between means and proportions of normal at two places or in different periods.
• to find the correlation between two variables X and Y such as in height or weight. For eg. Weight increases or decreases proportionately with height and if so by how much has to be found.

Biostatistics In Pharmacology

• To find the action of drug – a drug is given to animals or humans to see whether the changes produced are due to the drug or by chance.
• To compare the action of two different drugs or two successive dosages of the same drug.
• To find the relative potency of a new drug with respect to a standard drug
• To find action of drug.
• To compare action of two different drugs.
• To find relative potency of a new drug with respect to a standard drug.

In Medicine

• To compare efficacy of particular drug, operation or line of treatment.
• To find association between two attributes eg. Oral cancer and smoking
• To identify signs and symptoms of disease/ syndrome.

In clinical trials

• Clinical Trials
• Visit Clinical Trials.gov.
• Statistics in Drug Development
• Study design
• Clinical data
• Statistical modeling
• Other topics
• Statistics in Drug Development

Role of Biostatisticians

• Identify and develop treatments for disease and estimate their effects.
• Identify risk factors for diseases.
• Design, monitor, analyse, interpret, and report results of clinical studies.
• Develop statistical methodologies to address questions arising from medical/public health data.
• Locate, define & measure extent of disease
• Ultimate objective
• Improve the health of individual & community

Responsibilities of Biostatisticians

• Design the study and develop the protocol.
• Design the randomization algorithm if needed.
• Draft the statistical analysis plan (SAP) Programming the study data based on the SAP: tables/ figures/ listings (TFLs). 
• Present the results and write the interpretation of study results in clinical study report (CSR). Statistics in Drug Development

According to Lord Kelvin,
when you can measure what you are speaking about and express it in numbers, you know something about it but when you cannot measure, when you cannot express it in numbers, your knowledge is of meagre and unsatisfactory kind.