Clinic Exploratory research Preclinical

3D printing and artificial intelligence: the future of galenics?

“Ten years from now, no patient will take the same thing as another million people. And no doctor will prescribe the same thing to two patients.”

Fred Paretti from the 3D drug printing startup Multiply Labs.

3D printing – also known as additive manufacturing – is one of the technologies capable of transforming pharmaceutical development, and will certainly play a role in the digitalization of the drug manufacturing sector. This short article will attempt to provide an overview of how 3D printing works, its various use cases in the manufacture of personalized medicines, the current regulatory framework for this innovative technology, and the synergies that may exist with Artificial Intelligence.

3D printing, where do we stand?

The principle of 3D printing, developed since the early 2000s and now used in a large number of industrial fields, consists of superimposing layers of material in accordance with coordinates distributed along three axes (in three dimensions) following a digital file. This 3D file is cut into horizontal slices and sent to the 3D printer, allowing it to print one slice after another. The terminology “3D printing” brings together techniques that are very different from each other:

  • The deposition of molten wire or extrusion: a plastic wire is heated until it melts and deposited at points of interest, in successive layers, which are bound together by the plastic solidifying as it cools. This is the most common technique used by consumer printers.
  • The photopolymerization of the resin: a photosensitive resin is solidified with the help of a laser or a very concentrated light source, layer by layer. This is one of the techniques that allows a very high level of detail.
  • Sintering or powder fusion: a laser is used to agglomerate the powder particles with the energy it releases. This technique is used to produce metal or ceramic objects.

In the pharmaceutical industry, 3D printing is used in several ways, the main ones being :

  • The realization of medical devices, using the classic techniques of printing plastic or metallic compounds or more particular techniques allowing medical devices to acquire original properties, like the prostheses of the start-up Lattice Medical allowing adipose tissue to regenerate.
  • Bio-printing, allowing, by printing with human cells, to reconstitute organs such as skin or heart patches, like what is done by another French start-up: Poietis
  • Finally, and this is what will be discussed in this article, 3D printing also has a role to play in galenics by making it possible to print, from a mixture of excipient(s) and active substance(s), an orally administered drug.

What are the uses of 3D printing of medicines? 

3D printing brings an essential feature to drug manufacturing: flexibility. This flexibility is important for:

  • Manufacturing small clinical batches: clinical phases I and II often require small batches of experimental drugs for which 3D printing is useful: it is sometimes economically risky to make large investments in drug manufacturing at this stage. Moreover, it is often necessary to modify the active ingredient content of the drugs used, and 3D printing would enable these batches to be adapted in real time. Finally, 3D printing can also be useful for offering patients placebos that are as similar as possible to their usual treatments.
  • Advancing towards personalized medicine: 3D printing of drugs allows the creation of “à la carte” drugs by mixing several active ingredients with different contents for each patient. In the case of patients whose weight and absorption capacities vary over time (children or the elderly who are malnourished, for example), 3D printing could also adapt their treatments in real time according to changes in their weight, particularly in terms of dosage and speed of dissolution.

To address these issues, most major pharmaceutical companies are increasingly interested in 3D printing of drugs. They are investing massively in this field or setting up partnerships, like Merck, which is cooperating with the company AMCM in order to set up a printing system that complies with good manufacturing practices. The implementation of this solution has the potential to disrupt the traditional manufacturing scheme, as illustrated in the diagram below.

Figure 1 – Modification of the manufacturing steps of a tablet by implementing 3D printing (Source : Merck)


The first commercialized 3D printed drug was approved by the FDA in 2015. Its active ingredient is levetiracetam. The goal of using 3D printing for this drug was to achieve a more porous tablet that dissolves more easily and is more suitable for patients with swallowing disorders. Despite these initial approvals and market accesses, the regulatory environment has yet to be built, as it is still necessary to assess the changes in best practices that 3D printing technology may impose and determine what types of tests and controls should be implemented. Destructive quality controls are not particularly well suited to the small batches produced by the 3D printer technique. To our knowledge, there are currently no GMP-approved 3D printers for the manufacture of drugs.

Will the future of drug 3D printing involve artificial intelligence? 

A growing number of authors believe that 3D printing of drugs will only be able to move out of the laboratory and become a mainstream technology in industry if artificial intelligence is integrated. Indeed, as things stand at present, because of the great flexibility mentioned above, the use of 3D printing requires a long iterative phase: it is necessary to test thousands of factors concerning in particular the excipients used, but also the parameters of the printer and the printing technique to be selected. The choice of these different factors is currently made by the galenics team according to its objectives and constraints: what is the best combination of factors to meet a given pharmacokinetic criterion? Which ones allow to minimize the production costs? Which ones allow to respect a possible regulatory framework? Which ones allow for rapid production? This iterative phase is extremely time-consuming and capital-intensive, which contributes to making 3D printing of drugs incompatible with the imperatives of pharmaceutical development for the moment. Artificial Intelligence seems to be the easiest way to overcome this challenge and to make the multidimensional choice of parameters to be implemented according to the objectives “evidence-based”. Artificial Intelligence could also be involved in the quality control of the batches thus manufactured.

The use of Artificial Intelligence to design new drugs opens up the prospect of new technical challenges, particularly with regard to the availability of the data required for these Machine Learning models, which are often kept secret by pharmaceutical laboratories.  We can imagine that databases can be built by text-mining scientific articles and patents dealing with different galenic forms and different types of excipients and then completed experimentally, which will require a significant amount of time. In addition to these technical challenges, it will also be necessary to ask more ethical questions, particularly with regard to the disruption of responsibilities caused by the implementation of these new technologies: who would be responsible in the event of a non-compliant batch being released? The manufacturer of the 3D printer? The developer of the algorithm that designed the drug? The developer of the algorithm that validated the quality control? Or the pharmacist in charge of the laboratory?

All in all, we can conclude that 3D printing of medicines is a technology that is already well mastered, whose market is growing by 7% each year to reach a projected market of 440 million dollars in 2025, but whose usefulness is so far limited to certain cases of use, but which could tomorrow, due to the unlocking of its potential through the combination of Artificial Intelligence, allow us to achieve a fully automated and optimized galenic development and manufacturing of oral forms, finally adapted to the ultra-customized medicine that is coming.

To subscribe to the monthly newsletter for free: Registration

Would you like to take part in writing articles for the newsletter ? You wish to participate in an entrepreneurial project on these themes ?

Contact us at ! Join our LinkedIn Group !

To go further:

  • Moe Elbadawi, Laura E. McCoubrey, Francesca K.H. Gavins, Jun J. Ong, Alvaro Goyanes, Simon Gaisford, and Abdul W. Basit ; Disrupting 3D Printing of medicines with machine learning ; Trends in Pharmacological Sciences, September 2021, Vol 42, No.9
  • Moe Elbadawi, Brais Muñiz Castro, Francesca K H Gavins, Jun Jie Ong, Simon Gaisford, Gilberto Pérez , Abdul W Basit , Pedro Cabalar , Alvaro Goyanes ; M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines ; Int J Pharm. 2020 Nov 30;590:119837
  • Brais Muñiz Castro, Moe Elbadawi, Jun Jie Ong, Thomas Pollard, Zhe Song, Simon Gaisford, Gilberto Pérez, Abdul W Basit, Pedro Cabalar, Alvaro Goyanes ; Machine learning predicts 3D printing performance of over 900 drug delivery systems ; J Control Release. 2021 Sep 10;337:530-545. doi: 10.1016/j.jconrel.2021.07.046
  • Les médicaments imprimés en 3D sont-ils l’avenir de la médecine personnalisée ? ; 3D Natives, le média de l’impression 3D ;!
  • Les médicaments de demain seront-ils imprimés en 3D ? ; Le mag’ Lab santé Sanofi ;
  • Press Releases – Merck and AMCM / EOS Cooperate in 3D Printing of Tablets ;

Ces articles pourraient vous intéresser


Introduction to DeSci

How Science of the Future is being born before our eyes « [DeSci] transformed my research impact from a low-impact virology article every other year to saving the lives and…
Illustration In Silico

Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or…

By Alexandre Demailly

Pharmacist graduated from Lille University, France, Alexandre pursued his studies in Medical Economics at the Paris-Dauphine University and developed his knowledge of Artificial Intelligence in Health at the University of Paris.
Passionate about health innovation and entrepreneurship, Alexandre is currently involved in two early stage biotechs in the neurodegenerative diseases field.