Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), along with the World Health Organization (WHO), has provided a set of guidelines (ICH Q1A-E, Q3A-B, Q5C, Q6A-B) intended to unify the standards for the European Union, Japan, and the United States to facilitate the mutual acceptance of stability data that are sufficient for registration by the regulatory authorities in these jurisdictions. Overall, ICH stability studies involve a drug substance tested under storage conditions and assess its thermal stability and sensitivity to moisture. The long-term testing should be performed over a minimum of 12 months at 25 °C ± 2 °C/60% RH ± 5% RH or at 30 °C ± 2 °C/65% RH ± 5% RH. The intermediate and accelerated testing should cover a minimum of 6 months at 30 °C ± 2 °C/65% RH ± 5% RH (which is not necessary if this condition was utilized as a long-term one) and 40 °C ± 2 °C/75% RH ± 5% RH, respectively. Hence, the ICH stability testing for industrially fabricated medicines is rigorous and tedious and involves a long period of time to obtain preclinical stability data. For this reason, Accelerated Predictive Stability (APS) studies, carried out over a 3–4-week period and combining extreme temperatures and RH conditions (40–90 °C)/10–90% RH, have emerged as novel approaches to predict the long-term stability of pharmaceutical products in a more efficient and less time-consuming manner. In this work, the conventional ICH stability studies versus the APS approach will be reviewed, highlighting the advantages and disadvantages of both strategies. Furthermore, a comparison of the stability requirements for the commercialization of industrially fabricated medicines versus extemporaneous compounding formulations will be discussed.

The interindividual variability is an increasingly global problem when treating patients from different backgrounds with diverse customs, metabolism, and necessities. Dose adjustment is frequently based on empirical methods, and therefore, the chance of undesirable side effects to occur is high. Three-dimensional (3D) Printed medicines are revolutionsing the pharmaceutical market as potential tools to achieve personalised treatments adapted to the specific requirements of each patient, taking into account their age, weight, comorbidities, pharmacogenetic, and pharmacokinetic characteristics. Additive manufacturing or 3D printing consists of a wide range of techniques classified in many categories but only three of them are mostly used in the 3D printing of medicines: printing-based inkjet systems, nozzle-based deposition systems, and laser-based writing systems. There are several drawbacks when using each technique and also the type of polymers readily available do not always possess the optimal properties for every drug. The aim of this review is to give an overview about the current techniques employed in 3D printing medicines, highlighting their advantages, disadvantages, along with the polymer and drug requirements for a successful printing. The major application of these techniques will be also discussed.

3D printing technologies enable medicine customization adapted to patients’ needs. There are several 3D printing techniques available, but majority of dosage forms and medical devices are printed using nozzle-based extrusion, laser-writing systems, and powder binder jetting. 3D printing has been demonstrated for a broad range of applications in development and targeting solid, semi-solid, and locally applied or implanted medicines. 3D-printed solid dosage forms allow the combination of one or more drugs within the same solid dosage form to improve patient compliance, facilitate deglutition, tailor the release profile, or fabricate new medicines for which no dosage form is available. Sustained-release 3D-printed implants, stents, and medical devices have been used mainly for joint replacement therapies, medical prostheses, and cardiovascular applications. Locally applied medicines, such as wound dressing, microneedles, and medicated contact lenses, have also been manufactured using 3D printing techniques. The challenge is to select the 3D printing technique most suitable for each application and the type of pharmaceutical ink that should be developed that possesses the required physicochemical and biological performance. The integration of biopharmaceuticals and nanotechnology-based drugs along with 3D printing (“nanoprinting”) brings printed personalized nanomedicines within the most innovative perspectives for the coming years. Continuous manufacturing through the use of 3D-printed microfluidic chips facilitates their translation into clinical practice.