Razvoj metformin hidroklorida za izravnu kompresiju metodom sušenja raspršivanjem

Metformin hydrochloride exhibits poor compressibility during compaction, often resulting in weak and unacceptable tablets with a high tendency to cap. The purpose of this study was to develop directly compressible metformin hydrochloride by the spray drying technique in the presence of polymer. Metformin hydrochloride was dissolved in solutions containing a polymer, namely polyvinylpyrrolidone (PVP K30), in various concentrations ranging from 0-3 % m/V. These solutions were employed for spray-drying. Spray-dried drug was evaluated for yield, flow property and compressibility profile. Metformin hydrochloride spray-dried in the presence of 2 % PVP K30 showed an excellent flow property and compressibility profile. From the calculated Heckel’s parameter (Py = 2.086), it was demonstrated that the treated drug showed better particle arrangement in the initial compression stage. Kawakita analysis revealed better packability of the treated drug compared to the untreated drug. Differential scanning calorimetry and Fourier transform infrared spectroscopy experiments showed that the spray-dried drug did not undergo any chemical modifications. Tablets made from the spray-dried drug (90 %, m/m) were evaluated for crushing strength, friability and disintegration time and the results were found satisfactory.Metformin hidroklorid se teško komprimira zbog čega nastaju slabe tablete neodgovarajuće kvalitete s velikom tendencijom kalanja. Cilj ovog rada je prirediti metformin hidroklorid za izravnu kompresiju metodom sušenja raspršivanjem u prisutnosti polimera. Metformin hidroklorid je otopljen uz dodatak različitih količina (03 % m/V) polivinilpirolidona (PVP K30). Dobivene otopine sušene su raspršivanjem, a tako pripravljenom metformin hidrokloridu određivano je iskorištenje, tečnost i kompresibilnost. Metformin hidroklorid pripravljen u prisutnosti 2 % PVP K30 ima izvrsnu tečnost i kompresibilnost. Izračunati Heckelovi parametri (Py = 2,086) pokazuju da tako obrađeni metformin hidroklorid tvori veće čestice na početku kompresije. Analiza po Kawakiti ukazuje na to da se obrađeni lijek bolje preša od neobrađenog. Diferencijalna pretražna kalorimetrija (DSC) i Fourierova transformirana infracrvena spektroskopija (FTIR) pokazuju da sušenje raspršivanjem nije uzrokovalo nikakve kemijske promjene. Iz obrađenog metformina izrađene su tablete (90 % m/m) sa zadovoljavajućom lomljivošću, drobivošću i vremenom dezintegracije

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes

Application of Neural Network Models with Ultra-Small Samples to Optimize the Ultrasonic Consolidation Parameters for ‘PEI Adherend/Prepreg (CF-PEI Fabric)/PEI Adherend’ Lap Joints

The aim of this study was to optimize the ultrasonic consolidation (USC) parameters for ‘PEI adherend/Prepreg (CF-PEI fabric)/PEI adherend’ lap joints. For this purpose, artificial neural network (ANN) simulation was carried out. Two ANNs were trained using an ultra-small data sample, which did not provide acceptable predictive accuracy for the applied simulation methods. To solve this issue, it was proposed to artificially increase the learning sample by including additional data synthesized according to the knowledge and experience of experts. As a result, a relationship between the USC parameters and the functional characteristics of the lap joints was determined. The results of ANN simulation were successfully verified; the developed USC procedures were able to form a laminate with an even regular structure characterized by a minimum number of discontinuities and minimal damage to the consolidated components