AI And Data Pull CDMOs Into Pharma's Commercial Strategy

By Louis Garguilo, Chief Editor, Outsourced Pharma

The distance between external API producers and fill-finish experts, and the commercial apparatus of their pharma customers, has lessened.  

While powerful drug developers/producers, pharma organizations identify equally as astute commercial sales and marketing organizations. Now they require pervasive strategies to get their drugs and therapies directly to consumer-slash-patients.

Influencing all forms of marketing/sales today is the amount of data that can, and so is, generated, and how that data is interpreted and communicated to constituencies (starting with the regulatory bodies).

Moreover, clearer overall value analyses derived from early development processes and initial  productions is enabled by artificial intelligence tools, and advanced CDMOs.

Jon Williams, CEO, Lumanity, an organization focused on value demonstration, says pharma’s “sales and marketing muscle has dominated the commercialization agenda, but that must change to a world where evidence of defined value matters more.”

This reflects a desire to integrate development strategy with commercial decision-making early in the product lifecycle. Commercial evidence starts as innovation becomes tangible material, set processes, and then product creation.

Mostly, this happens at external development and manufacturing partners. The best CDMOs tune into what's required of the commercial and patient-consumer markets throughout their relationships with sponsors.

And they tune on the AI integration, to assist with precise controls, interoperability, and translation of "high-integrity manufacturing and real-world data," says Williams.

CDMOs converting process and production data into clinically and economically relevant evidence – impacting time-to-market, supply reliability, and differentiated outcomes – shape (not just support) this commercial-value evidence narrative.

Biotechs, for example, should invite CDMOs into their earliest development and future commercialization ideas.

Anticipating processing/manufacturing at scale, and peering into potential markets and sales of product, will, for one thing among a host of positives, provide biotechs the best chances at successful exit strategies.

All sponsors outsourcing within a strict regulatory environment; where AI enables massive data collection and accelerated analysis to assess overall value propositions and better decisions, will need external partners present at  "internal deliberations."   

The Value Proposition

Williams conceptualizes this as value defined by data. The entire commercialization model of pharma begins to reorient itself around this proposition.

This implies early, calculated financial value for moving projects along. It requires increased clinical-trial valuation with a deeper understanding of patient outcomes,  and solid understanding of commercial value – COGs and margins and extrapolated patient pools.  

Data originates from and impacts outsourcing relationships; it's an early component of business economics and future addressable markets. CDMOs enhance comparative effectiveness of production, product, and promotion.

Data-based decisions elevate outcomes for regulators, payers, providers, and patients, and provide more meaningful and translatable information.   

Williams points out that stakeholders have become more sophisticated and insistent. Everyone wants clear answers to their questions.

CDMOs converting process and production data into clinically and economically relevant evidence – impacting time-to-market, supply reliability, and differentiated outcomes – help shape the commercial and value-evidence narrative.

This, though, introduces structural questions around data ownership, governance, and the degree to which pharma is willing to integrate external partners into early strategic decision-making.

Not all CDMOs will be positioned to make this transition.

Those lacking digital infrastructure, and data-analytics capabilities, risk remaining mere "capacity providers" in an increasingly evidence-driven model. 

The New Commercial Model

A newer component of this change is the rise of the medical-science liaison (as "sales" representative). These proliferating professionals shift from traditional interactions towards more routine data-driven and scientific conversations.

Armed with detailed (but clear) explanations of value as well as efficacy and safety, these new-age sherpas set out to deliver a more personally detailed message to individual consumer-patients.

CDMOs, for their part, embed advanced AI tools throughout their organization, while employing their sentient beings to provide oversight and human-intuitive analytical decisions.

Knowing you must demonstrate real-world value based on ubiquitous data, clarifying economic impact, and differentiated outcomes, sponsors begin to design programs differently from the research stage.

You become circumspect about who your CDMO is – and how to involve them in your commercial plans and strategies (as well as regulatory approaches). 

AI adds a dimension. Strict standards that already exist around data integrity and demonstrative regulatory compliance are now both magnified, and more dispersed. 

Biopharma marketing and selling shifts from product messaging to value measuring.

With value demonstration the new currency, to succeed sponsors will select the optimal development and manufacturing partners to work this new paradigm.