The regulatory landscape of M&As in the pharma industry

The importance of M&As in the pharma industry

Mergers and acquisitions (M&As) have great strategic importance for pharmaceutical companies.

In recent times, the pharma industry witnessed a significant uptick in mergers and acquisitions and a modest increase in the average deal size on the top end of the spectrum.

M&As pose enormous challenges on the regulatory front for pharma companies as the industry is highly regulated. In most cases, companies need to be better equipped to tackle these complexities. 

Due diligence is paramount and needs to be consistent to ensure the organization has a robust plan for managing the M&A. Another challenge is selective data migration, considering that organizations often use different systems and applications. So, amalgamation and data management of various applications are enormous challenges.

One of the traditional management practices of mergers and acquisitions is outsourcing M&A activities to CROs. However, not all organizations are able to navigate the complex landscape of regulatory M&A processes successfully. 

The rapidly evolving landscape 

Industry 4.0 technologies can help manage mergers and acquisitions effectively. 

By harnessing the power of Industry 4.0 technologies, such as AI, machine learning (ML), Generative AI, automation, and robotics, companies can streamline the due diligence process, identify synergies, and assess potential risks with greater accuracy and speed. This will ensure faster approvals from the statutory bodies enabling quicker access of drugs and other medicinal products to patients.

These technologies can facilitate data integration, enhance decision-making capabilities, and automate repetitive tasks allowing the regulatory affairs team to focus on strategic initiatives.

Furthermore, the insights derived by AI-ML help implement a knowledge-driven approach in managing M&A, optimizing outcomes, and creating competitive advantage in the dynamic business landscape.

Technology enablers 

Pharma companies can adopt a few approaches to make mergers and acquisitions seamless from a regulatory perspective. 

The regulatory activities for M&A involve the submission of marketing authorization transfer (MAT) applications to the health authorities like the US FDA (US Food and Drug Administration) and EMA (European Medicinal Agency). 

Creation of the MAT applications requires data extraction from diverse sources, review of the data, and finally, using this data to create MAT applications per standards.

The technology enablers can aid the creation of MAT applications for submission to health authorities. This can be done through the following steps:

1. Data extraction: AI-ML algorithms can analyze complex data derived from diverse sources, and through NLP techniques, AI models can extract the relevant information. This will save efforts incurred in tedious manual data extraction.
2. Data integration: The data used for authoring MAT applications resides in multiple systems and databases. AI-ML can help integrate and harmonize the data and ensure consistency. This helps create a centralized and comprehensive database, which can be used as a single source of truth for authoring. Additionally, this step saves time spent searching for data in different locations.
3. Creation of MAT application: A trained AI-ML tool can author MAT applications in adherence to ontologies and standards based on the data and information available in a centralized database. This will enable the resources to focus on the review and validation of the application and reduce the manual efforts in data entry and compilation. 
4. Compliance checks: AI-ML-enabled tools perform automated compliance checks on the reports, verifying that the data is complete, accurate, and meets the regulatory purpose. The system flags potential errors or inconsistencies for manual review by the regulatory team. This saves time and effort in reviewing MAT applications and ensures the submission of error-free information to the health authorities.

A real-world example of report creation using technology enablers is the collaboration between a technology company and a European pharma major. For regulatory submissions, the natural language generation (NLG) platform built by the technology company extracts data from various sources and transforms it into clear and concise narratives to generate clinical reports. The platform uses AI-ML algorithms to analyze structured and unstructured data such as clinical information, safety reports, and regulatory guidelines. 

The idea is to use a similar approach to create reports for submissions necessary for mergers and acquisitions. 

Some of the strategic imperatives identified for efficient management of mergers and acquisitions are listed below:

• Intelligent planning​

By consolidating the regulatory requirements for M&A from connected sources, the (user) can create smart submission plans and facilitate the reuse of the plans. This AI-ML application will be able to correlate regulatory intelligence with internal precedence to suggest strategies for regulatory submissions for M&A to the user.

The regulatory affairs team will plan and manage submissions using the BPM (business process management) tool using persona-based intelligent dashboards.

• Smart data management​

This will promote connected business systems to seamlessly exchange meaningful data or information over intelligent and secure network systems. The idea is to bring the data required for regulatory submissions of mergers and acquisitions to a smart data fabric, which ontologies and logical frameworks can leverage to create regulatory submission outputs as per the submission plan. 

• Assisted automated authoring​

The approach entails automating structured content creation for regulatory submission using structured data.​ In this case, with the help of the AI-ML algorithm, the available data will be converted into meaningful text for regulatory submissions. An automated authoring application will leverage a knowledge graph-enabled intelligent system equipped with built-in ontology to create NLG-based text output. This can be adapted to author regulatory submissions.