Blockchain, Data Integrity & Computer System Validation

Can we move away from our current human-to-human-based trust to a digital trust ecosystem built on machine-to-machine interactions?

Blockchain, Data Integrity & Computer System Validation

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Blockchain and Data Integrity in Computer System Validation

Imagine a machine with thousands of pieces that are extremely vital and useful for a variety of reasons. It is reliable and can handle many tasks at once. However, the machine is made up of extremely minute pieces, each of which is the property of a different person. The machine will not operate until every member is there and has that exact part. A missing cog, and even if everyone is present, the machine will not work. This machine is a blockchain, and these people with specialised pieces are business partners. They contain particular keys required for the blockchain to function, alter, acquire, encode, and decode data.

The pharmaceutical industry is an industry built inherently on distrust.

The regulators distrust the manufacturers, the manufacturers distrust the employees. Employees distrust every other actor in the supply chain. And for good reason - to ensure the safety of vulnerable patients where a single slight misstep anywhere in the myriad of complex supply chains can result in life-changing consequences or even death

Therefore, all pharmaceutical manufacturers must comply with good manufacturing practices while ensuring data integrity. Data integrity enables good decision-making by pharmaceutical manufacturers and regulatory authorities.

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These rules and regulations laid down by the regulatory bodies dictating data integrity can be summed up by the ALCOA principle.

Data supporting the quality and safety of products must meet the ALCOA elements to avoid regulatory citations for data integrity issues

At present relevant GxP data is stored in databases, which undergoes validation to ensure its integrity according to regulatory agency standards.

From data storage and security point of view in the Life Sciences industries, Blockchain technology offers 3 essential conditions to ensure data integrity: security, action traceability and transparency in data handling.

In 2022, blockchain technology is not novel, everyone who has a smartphone has either heard about, working on or using this technology. People operating in the cryptocurrencies and bitcoin niche are particularly familiar with the concept. However, this is just the tip of the iceberg; blockchain is something so vast that people are only now seeing its full potential. However, one of the most significant advantages of this technology is data integrity and authenticity.

Sounds complicated?

Let’s simplify it by giving you an example!

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What is Merkle Tree?

A Merkle Tree is a structure of interconnected information bits known as a "hash" by data scientists. Each hash in a blockchain is part of a larger equation or job; each hash must be in the right place at the right time for the process or blockchain to function.

These Merkle Tree components, also known as hashes, are critical to the success of blockchain technology. Because each block in a blockchain is unique and is coded in a precise way, no new hash or part can be added or removed unless all participants in the chain agree.

Auditing Data in Blockchain

Similarly, in traditional data sciences, if auditing data accessible on a node is required, the system manager or auditor manually checks all transactions, and changes, and validates each step, making the process time-consuming. This procedure is performed for each transaction. Imagine processing billions of transactions; it would take an eternity. In blockchain technology, however, the system has already been audited, and all parties are aware of where the adjustments have been made, because no one partner may edit the data stream without the agreement of all.

This, in turn, introduces a completely new concept of data trust, validity, and integrity. Assume there are three parties involved in a blockchain transaction, and they are tracing a supply chain, such as a pharmaceutical product. With blockchain, each party can observe the transaction and movement of the material, but none of them may modify the system information without the agreement of all three parties. As a result, data on a blockchain is not only secure but also immutable. Because of this, blockchain technology has become a pioneer of a whole new set of technologies that are based on creating trust through its "hashes."

Application of Blockchain

Where can we apply this approach in the world? This technology has as many applications as one may wish. If you want to keep shared data that is also entirely safe and secure against unilateral alterations and hackers, blockchain is the solution. It may serve the same role as documents, corporate assets, trade secrets, supply chain, and patient medical information entrusted to their healthcare provider.

The current systems based on Merkle Tree or hash stacking are not only practical, but also fundamentally transparent.

The requirements for the Audit Trail and its review are formulated in Annex 11 of the EU GMP Guidelines: 9 Audit Trails: "Consideration should be given, based on a risk assessment, to building into the system the creation of a record of all GMP-relevant changes and deletions (a system generated "audit trail")

Each piece of equipment that generates data that is used in production or to make a decision relating to drug quality must provide a fully validated audit trail.

What if we could combine these disparate audit trails into a robust system that followed the ALCOA principles?

ServBlock allows for Data security, traceability, and trust between counterparties that current industrial complexes lack.


·       Kasten, J. E. (2020). Engineering and manufacturing on the blockchain: A systematic review. IEEE Engineering Management Review, 48(1), 31-47.

·       Li, H., Lu, R., Zhou, L., Yang, B., & Shen, X. (2013). An efficient merkle-tree-based authentication scheme for smart grid. IEEE Systems Journal, 8(2), 655-663.

·       Yazdinejad, A., Srivastava, G., Parizi, R. M., Dehghantanha, A., Choo, K. K. R., & Aledhari, M. (2020). Decentralized authentication of distributed patients in hospital networks using blockchain. IEEE journal of biomedical and health informatics, 24(8), 2146-2156.

·       Moinet, A., Darties, B., & Baril, J. L. (2017). Blockchain based trust & authentication for decentralized sensor networks. arXiv preprint arXiv:1706.01730.