Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.Algoritmi mašinskog učenja, kao i veštačka inteligencija u širem smislu, su veoma značajni i primenjuju se u razne svrhe u okviru farmaceutske tehnologije. Počevši od razvoja formulacija, preko izuzetnog potencijala za integraciju u koncept dizajna kvaliteta (engl. Quality by design), algoritmi mašinskog učenja omogućavaju bolje razumevanje uticaja kako formulacionih faktora tako i odgovarajućih procesnih parametara. Algoritmi mašinskog učenja mogu biti od naročitog značaja i za analizu velikog obima podataka koji se generišu korišćenjem procesnih analitičkih tehnologija. U ovom radu su ukratko predstavljene veštačke neuronske mreže, kao jedan od najčešće korišćenih algoritama mašinskog učenja. Prikazani su procesi treninga i testiranja mreža, kao i ilustrativni primeri algoritama primenjenih za različite potrebe razvoja i/ili optimizacije farmaceutskih formulacija i postupaka njihove izrade. Takođe, dat je i pregled budućih trendova u ovoj oblasti, kao i novijih studija o sofisticiranim metodama, poput dubokih neuronskih mreža, i light gradient boosting algoritma. Zainteresovani čitaoci se takođe upućuju na nekoliko zvaničnih dokumenata (vodiča), po uzoru na koje mogu da se očekuju i preporuke za strukturiranu prezentaciju modela mašinskog učenja koji će se podnositi regulatornim telima u okviru dokumentacije koja se priprema za potrebe registracije novih lekova

Disrupting 3D printing of medicines with machine learning.

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future

Brain network signatures of depressive symptoms

Depressive symptoms are common in the general population. Even in individuals who do not meet the criteria for a Major Depression Disorder (MDD), their symptoms are of clinical relevance because they increase the likelihood of progressing into a full-blown depressive episode, which in turn increases the prevalence of future episodes. The studies in this thesis apply advanced computational methods to functional magnetic resonance imaging (fMRI) data to investigate the dynamics of network connectivity, with the aim of understanding what brain mechanisms make a person more vulnerable to depression. Our results suggest that imbalances in whole-brain connectivity can already be linked to higher levels of depressive symptoms in healthy (undiagnosed) individuals. These imbalances correspond to a reduced dynamism in the overall functional organization of the brain, suggesting a link between a ‘rigid brain’ and rigid behavior, such as the lack of flexibility in cognitive and emotional responses that often accompanies depressive symptoms. Additionally, individual differences in the repertoire of brain states indicate that people with more depressive symptoms engage more in states related to self-referential thinking. This tendency was also observed in remitted patients during the transition into a depressive episode. This emphasizes that the present experience of depressive symptoms, whether in healthy individuals or MDD patients, is associated with changes in brain communication. The findings of this thesis lead to a deeper understanding of the complex orchestration of brain communication and its changes concerning depressive symptomatology in clinical and nonclinical populations

The development and assessment of a generic carbamazepine sustained release dosage form

Carbamazepine (CBZ) is a first-line drug used for the treatment of partial and tonic-clonic seizures. It is also the drug of choice for use during pregnancy and recommended for the treatment of seizure disorders in children. CBZ possesses the ability to induce metabolism of drugs that are transformed in the liver and has the unique ability to induce its own metabolism by a phenomenon known as ‘auto- induction’, where its biological half-life is significantly reduced during chronic administration. Large doses of CBZ are often prescribed as daily divided doses and this often adversely affects patient compliance, with the result that therapy is ineffective. A sustained-release dosage form containing CBZ is currently marketed as Tegretol® CR and the development of a generic product would provide patients with an equivalent product with a similar dosing frequency, at a reduced cost. Therefore, the development of a polymer-based matrix tablet was undertaken to produce a sustained-release dosage form of CBZ, since these dosage forms are relatively simple and cheap to produce when compared to other, more sophisticated forms of sustained-release technology. Preformulation studies were conducted to assess moisture content of excipients and dosage forms and to identify possible incompatibilities between CBZ and potential formulation excipients. Furthermore, studies were conducted to assess the potential for polymorphic transitions to occur during manufacture. Stability testing was conducted to assess the behaviour of the dosage forms under storage conditions that the product may be exposed to. Dissolution testing was undertaken using USP Apparatus 3, which allowed for a more realistic assessment and prediction of in vivo drug release rates. Samples were analysed using a high performance liquid chromatographic method that was developed and validated for the determination of CBZ. Tablets were manufactured by wet granulation and direct compression techniques, and the resultant drug release profiles were evaluated statistically by means of the f1 and f2 difference and similarity factors. The f2 factor was incorporated as an assessment criterion in the design of an artificial neural network that was used to predict drug release profiles and formulation composition. A direct compression tablet formulation was successfully adapted from a prototype wet granulation matrix formulation and a number of formulation variables were assessed to establish their effect(s) on the dissolution rate profile of CBZ that resulted from testing of the dosage forms. The particle size grade of CBZ was also investigated and it was ascertained that fine particle size grade CBZ showed improved drug release profiles when compared to the coarse grade CBZ which was desirable, since CBZ is a highly water insoluble compound. Furthermore, the impact of the viscosity grade and proportion of rate-controlling polymer, viz., hydroxypropyl methylcellulose was also investigated for its effect on drug release rates. The lower viscosity grade was found to be more appropriate for use with CBZ. The type of anti-frictional agent used in the formulations did not appear to affect drug release from the polymeric matrix tablets, however specific compounds may have an effect on the physical characteristics of the polymeric tablets. The resultant formulations did not display zero-order drug release kinetics and a first-order mathematical model was developed to provide an additional resource for athematical analysis of dissolution profiles. An artificial neural network was designed, developed and applied to predict dissolution rate profiles for formulation. Furthermore, the network was used to predict formulation compositions that would produce drug release profiles comparable to the reference product, Tegretol® CR. The formulation composition predicted by the network to match the dissolution profile of the innovator product was manufactured and tested in vitro. The formulation was further manipulated, empirically, so as to match the in vitro dissolution rate profile of Tegretol® CR, more completely. The test tablets that were produced were tested in two health male volunteers using Tegretol® CR 400mg as the reference product. The batch used for this “proof of concept” biostudy was produced in accordance with cGMP guidelines and the protocol in accordance with ICH guidelines. The test matrix tablets revealed in vivo bioavailability profiles for CBZ, however, bioequivalence between the test and reference product could not be established. It can be concluded that the polymeric matrix CBZ tablets have the potential to be used as a twice-daily dosage form for the treatment of relevant seizure disorders