Biopharma Supply chain
Pharmaceutical supply chains are increasingly complex and highly regulated. The whole industry is facing pressure to perform and as a result, having an agile and robust supplier base is becoming essential. The number of counterfeit drugs and
product recalls as well as sophisticated cybercrime attacks are also highlighting the need for supply chains to be transparent with close stakeholder collaboration. pharma companies are regularly being targeted by cyber-criminals. Whether
intellectual property, private patient health data or commercially sensitive information. There is huge scope for hackers to obtain valuable data and so pharma companies are on high alert to mitigate risks and protect their customers.
In the light of these varying challenges, collaboration is becoming ever so important. Pharma supply chain relationships needs to be solidified, so that information is securely immutable, traceable and transparent across the value chain.
Key Challenges
.png)
Drug Discovery
Approaches in predicting drug efficacy in clinical trials with data precision, that avoids violating data privacy
Potential breach of research lab data and drug formula
Verifying user identity to access confidential data
Utilizing the correct and precise data protocols in order to identify the right target, the right molecule, the right tissue, the right safety with the right patient
.png)
Drug Development
Greater emphasis on human precision data may lead to improved target identification and validation
Inadequate collaboration among academia, industry and government.
Clinical trial results manipulation
Paper submission of NDA in certain countries that are not verifiable through a digital format
.png)
Drug Manufacturing
Precise & consistent data to accurately modelling the change in line with resources, knowing exactly when capacity adjustment is required
Manufacturing of any kind of drug, from genetics to innovative new treatments, including biologics, demand data quality at each stage
Tampering of chemical formula’s and improper mix of ingredients
Tracking the origins of potential compounds
Drug Manufacturing
Precise & consistent data to accurately modelling the change in line with resources, knowing exactly when capacity adjustment is required
Manufacturing of any kind of drug, from genetics to innovative new treatments, including biologics, demand data quality at each stage
Tampering of chemical formula’s and improper mix of ingredients
Tracking the origins of potential compounds
.png)
Supply chain
Supply chain structure has been changed to supply chain network with more complex structure including a higher level of interdependence and collaboration between various entities.
To effectively track & trace drugs through complex drug supply chain
Third party required to verify the fulfillment of contract terms
The ability to effectively increase or decrease aggregate production in response to customer demand changes
.png)
Sales & Marketing
Data handling, interpretation & consent – majority of healthcare organizations are unprepared to deal with emerging data sources or access to high volume of data. There is also a challenge with the lack of inability to understand datasets, and how it
can be applied to provide value within the sales organization
Solution overview
End-2-End drug supply chain management system integrating blockchain and federated learning on the Terracuda.Ai platform
.png)
Client Applications
.png)
Automated management & orchestration platform
.png)
Drug supplier
.png)
.png)
Manufacturer
.png)
Distributor
.png)
Hospitals
.png)
Pharmacies
.png)
Patients
.png)
API
Terracuda.Ai Platform
Access management / Smart contracts / Data encryption
.png)
Validation & Recommendation
Blockchain network
Federal learning
Raw materials
Drug
Order
Records
.png)
.png)
Datasets / Algorithms
Training test data
Trained models across hospitals
Drug recommendation
.png)
Terracuda.Ai solution is separated into 2 modules. The first is the tracking of the drug supply chain management system (executed on the consortium blockchain) and the 2nd module is the recommendation system (executed via Federated learning)
where both modules are fully integrated.
Blockchain solution on the Terraduda.Ai solution ensure the entire drug supply chain is foul proof and prevent counterfeit drugs entering the supply chain altogether. Whereas federated learning techniques are applied by using datasets
from patients across multiple hospitals that monitors patients health conditions when specific type of drugs are administered over a period of time.
.png)
Once these models are trained with predictions, this will allow us to understand the efficacy of the drug effectiveness in recommending the safest option to the pharmaceutical industry. This solution provides a complete end-2-end process in
building security & trust, from traceability of the drug within the supply chain, validating the authenticity of the drug, through to accuracy, recommending the efficacy of the drug.
Pharmaceutical companies are increasingly looking to Federated learning and blockchain, for development of novel drug design, through to manufacturing and the distribution within the supply chain process.
