The biopharmaceutical industry is at a crossroads when it comes to drug development. On the one hand, this time-intensive process of navigating a molecule successfully through the preclinical and clinical stages all the way to market has been saddled with high capital costs and stringent regulation; two factors that justify both a risk-averse approach and an urge to rein in R&D expenses.

And yet, as the sector re-evaluates the traditional blockbuster model and new legislation has incentivized commercial investment in
orphan and rare diseases, companies find themselves having to identify new strategies that can add both quality and efficiency to drug development.

There are no less challenges when it comes to drug delivery. Patient centricity and improving adherence are strong themes running through recent innovations in dosage forms and delivery systems and once again pharma finds itself seeking novel solutions to very old problems in a tightly regulated environment.

But there are signs of a sea change on the horizon as the science behind identifying lead compounds evolves. Young biotech firms have
built biomedical platforms that have yielded successful new treatments such as DNA- and cell-based therapies.

Cell and gene therapy development is a particularly exciting growth area as the list of medicines close to gaining regulatory approval racks up. These novel treatments have the potential to treat an enormous range of conditions in the fields of oncology and rare genetic disorders and in som cases to even cure diseases with a single use.

The Future is Digital

While the industry has overall been slow to utilize digital technology in drug development, the sheer complexities around the process –
especially in terms of data management -- make it a fertile stamping ground for future innovation.

Having made an impact in just about every other industry, Artificial Intelligence (AI) -- which aims to tackle complex problems in ways similar to human logic and reasoning –  is also making inroads into pharmaceutical development and delivery. Efforts to place the patient at the center of drug development decisions continue to gain steam, as do lingering challenges from solubility and bioavailability.

Although AI in drug development isn’t an entirely new concept – in silico design methods have been around for many years – it
does present one of the greatest opportunities for the industry. Coupled with the advent of big data, cloud-based computing, and the Industrial Internet of Things (IIoT), AI is being applied to bring new therapeutics to market. Currently, almost 200 startups are utilizing AI (or using AI as a key differentiator) to develop drugs or products; this is up substantially from approximately 40 in late 2017[i].

There are many advantages to this use of AI: cost, speed, and the ability to identify new molecules being chief among them. But, as with any trend, the promise of AI must be proven through continuous success. There is plenty of investment in this space; Signify Research recently reported that venture capital investment in AI for drug development and clinical trials reached $5.2 billion[ii].

While acknowledging investors are becoming more cautious, the research company says pharma partnerships “will provide access to a larger pool of data to train the algorithms and discover new valuable connections in disease and treatment mechanisms that wouldn’t have been possible otherwise with traditional methods.”  Indeed, a recent panel discussion at the CPhI Worldwide meeting in Frankfurt, Germany in November 2019 highlighted the tremendous impact that AI could have on drug development, particularly around molecular selection and design.

Other experts predict that the Internet of Things will have a strong influence on the pharmaceutical industry, as highlighted by the Pharma
4.0 initiatives that are being brought online.

Bikash Chatterjee, president and CSO at Pharmatech Associates, reports that Pharma 4.0 will profoundly transform a reactive manufacturing and overall drug development supply chain to a predictive framework based on analytics. He warns the benefits of such a change won’t be realized without explicit corporate goals and an appropriate organizational context.[iii].

Delivering on Patient Centricity

There has been considerable discussion about patient-centric drug delivery, especially for geriatric patients who not only form the largest user-group for medicines, but also represent the fastest-growing demographic. Placing patients at the center of drug development and delivery decisions means future drugs will be easier to take and improve patient compliance. It also enables the medicinal movement from hospital-centered care to care that is more home-based.

Another trend in drug delivery is the push by CDMOs to be truly full service. In addition to providing drug development and manufacturing, CDMOs are expanding to also offer dosage and delivery capabilities to their clients.

“There is a bit of an overlap here with the contract manufacturing side,” says Peter Shapiro, Senior Director at GlobalData Plc. 
“Small-molecule API manufacture is becoming quite commoditized right now, and many companies in the CDMO space are moving into dosage and advanced technologies.”

Evidence of this includes the November acquisition of Consort Medical, a British drug-delivery device and manufacturing services
provider, by Recipharm AB, a Swedish-based CDMO. Consort, back in 2014, had itself added to its drug delivery business with the acquisition of UK-based CDMO Aesica.

It is also possible that the strategy to bring together drug production and drug delivery is fueled by the new generation of cell and gene therapies.  There is a technical element, in terms of delivery, that these therapies require to bring them to the patient.  It is obvious that companies can make these potential new drugs but delivering them into the cells of patients is the larger technical challenge – circling back to patient centricity.

Beyond Solubility Challenges

Many of today’s new and complex therapies still face old challenges: solubility and bioavailability.

“Solubility continues to be problematic, yet the technologies to counteract this are becoming more interesting, and more companies are expanding their capabilities in this space,” GlobalData’s Shapiro says. “Because solubility is so problematic, there are many molecules that get disregarded – it’s amazing how many [molecules] don’t even make it to later stages.  And because more and more molecules are
having solubility issues, many people are investing heavily in powder and capsule technology to overcome the challenges of poorly soluble molecules.”

One area that has seen considerable activity, because of its ability to substantially improve physiochemical solid-state properties (e.g. solubility) of an API, is cocrystallization.  A pharmaceutical cocrystal is a single crystalline solid that incorporates two neutral molecules:
one being an active pharmaceutical ingredient (API) and the other being a cocrystal former, which may be an excipient or another drug.  Proper selection of the cocrystal former is key to the design and use of cocrystals, and screening many candidates is important.  Cocrystals are unique, in that they do not affect the pharmacological properties of the API but simply improve the bioavailability, as well as other physiochemical characteristics such as permeability, solubility, melting point, etc.

The interest in cocrystals has been supported by advances in co-crystal discovery, development, and approval, as well as supportive FDA guidance issued in February 2018.  Andy Singh, founder and director of Asha Pharma, recently highlighted a case study where more than 90 cocrystal experiments with 19 coformers led to an improved melting point for a particular drug, one that would not have moved forward into clinic without improved physiochemical properties[iv].

Often, even if the solubility challenge is solved, pharmaceutical companies can still encounter other liabilities at a later stage of development. A second challenge is the human body’s approach to metabolizing drugs – metabolism obviously has a tremendous impact on the efficacy of the drug.  One approach to overcoming this challenge is PDPK modelling, which can establish what other challenges a molecule may have later down the line, essentially de-risking the project.

Delicate balancing act

With the industry tasked with a delicate balancing act of managing risk, investment, regulation and demands for transparency against the need to modernize drug development and delivery frameworks to keep abreast of the emerging and requirements, change is unlikely to be quick.

However, there is general consensus that pharma does need to embrace changes in drug development and discovery to fully explore the wide range of opportunities that exist. Stronger clinical trial designs that focus on the patient, virtual trials, better capture and analysis of real-world data and evidence and smart, connected devices are all areas where advances can be made. The extent to which pharma embraces digital technology in drug development and delivery in the future may depend on the perceived success of pioneering projects as well as the operational challenges of modernizing this crucial phase of the product lifecycle.

Want to know more about drug development and delivery?

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References:

[i] https://blog.benchsci.com/startups-using-artificial-intelligence-in-drug-discovery

[ii] https://www.signifyresearch.net/healthcare-it/vc-funding-ai-drug-development-clinical-trials-hits-5-2b/

[iii] Building Quality into Pharma Manufacturing, from Molecule to Medicine: Pharma 4.0, Bikash Chatterjee, President and Chief Science Officer, Pharmatech Associates, 2019 CPhI Annual Report, pp.12-19.

[iv] Cocrystal Discovery & Selection to Improve Solid State Properties of an API, Poster Session, American Association of Pharmaceutical Scientists (AAPS) Annual Meeting, San Antonio, TX (November 5, 2019).