Data Analytics
Jan 14, 2019
| 3 min read

Reducing Batch-to-Batch Variability of Botanical Drug Products

The natural variability of botanical material often makes it difficult to ensure a consistent quality process for pharmaceuticals made from plant-based products. In addition, botanical drug products (BDPs) are often produced using a series of separate batch processes, which adds even more variability into the manufacturing process.

This article is posted on our Science Snippets Blog.

Variability in raw material and batch processes can make producing a consistent botanical drug product for commercial pharmaceutical use difficult.

Botanicals have been used in health care around the world for thousands of years, and China, in particular, has a long history of using herbal medicines.¹ In recent decades, the adaption of botanicals for use as pharmaceuticals around the globe has expanded greatly. But this has led to increased scrutiny by the U.S. Food and Drug Administration (FDA) and other regulatory agencies about how to define the quality attributes of a drug.

Because the relationship of product attributes to product quality has not always been well understood, the FDA has ensured quality by applying specifications based on observed properties or by constraining manufacturing to tight processes.²

In a a 2004 paper ³, Janet Woodcock (FDA Director for the Center for Drug Evaluation and Research) defined pharmaceutical quality as “a product that is free of contamination and reproducibly delivers the therapeutic benefit promised in the label to the consumer.”

Variability Leads to Batch-to-Batch Inconsistencies

Yet, the natural variability of botanical raw materials, together with current manufacturing process, can lead to batch-to-batch product quality variability. Some of the factors that can influence the quality of botanical raw materials include: climate, fertilization methods, harvest time, and storage conditions. As a result, both the chemical composition and biological activity of the raw materials can be difficult to categorize and vary widely from sample to sample.

Then a variety of processing procedures, from heating to adding bases or acids, can affect the raw materials in unpredictable ways. This makes it difficult to ensure batch-to-batch consistency and adherence to defined processes that regulatory agencies require for approval.

But data analytics offers a solution. Multivariate statistical analysis provides a method to help map process parameters and raw material characteristics to specific quality attributes, allowing manufacturers to adjust production processes in a consistent way to ensure product efficacy and quality.

Implementing Multivariate Data Analysis

Using multivariate data analytics tools, pharmaceutical manufacturers can better define quality control parameters and use continuous process verification to ensure the quality of the final product. Using real-time data analytics, it’s possible to reduce batch-to-batch variability and adjust process parameters during manufacturing to prevent eventual process deviations from affecting product quality.

The first step is to begin gathering and storing data on process variables in a real-time database. Next, a batch evolution model can be built using a multivariate data analytics tool such as SIMCA®.

Then data analysis is used to gain a deeper insight of the process. For example, it’s possible to identify variations caused by different operators managing the equipment as well as identify the critical process variables that have a significant impact on product quality.

Building a Golden-Batch Model

The next step is building an ideal model (a “golden-batch” model) using data from good-behavior batches identified in the first step. The golden-batch model can then be used with a real-time data analytics monitoring tool such as SIMCA®-online to detect any deviations in the batch processes.

Preventing Ruined Batches

With SIMCA®-online, operators have an easy-to-use visual tool that helps them monitor the process and makes it possible to take corrective steps when necessary. In this way, operators can fix problems before they affect product quality or ruin entire batches.

Reducing Batch-to-Batch Variability

Tasly, a Chinese pharmaceutical company producing traditional Chinese medicine in the form of botanical drug products (BDPs), recently implemented multivariate data analysis and real-time monitoring of its production process to improve process control and to ensure a consistent product quality.

Monitoring the process in real-time has enabled Tasly to ensure a more consistent product quality and reduce batch-to-batch variability.

Find out more by downloading the case study.

Get Case Story


  1. The Traditional Medicine and Modern Medicine from Natural Products. Molecules 2016, 21, 559; doi:10.3390/molecules21050559
  2. Botanical Drug Development Guidance for Industry, U.S. Department of Health and Human Services Food and Drug Administration, Center for Drug Evaluation and Research (CDER), December 2016 Pharmaceutical Quality/CMC Revision 1
  3. J. Woodcock. The concept of pharmaceutical quality. Am. Pharm. Rev.Nov/Dec 2004:pp. 1–3.
  4. The worldwide trend of using botanical drugs and strategies for developing global drugs. BMB Rep. 2017 Mar; 50(3): 111–116.
  5. Batch-to-Batch Quality Consistency Evaluation of Botanical Drug Products Using Multivariate Statistical Analysis of the Chromatographic. Fingerprint, AAPS, PharmSciTech. Vol. 14, No. 2. June 2013