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

Effect of the Energy Director Material on the Structure and Properties of Ultrasonic Welded Lap Joints of PEI Plates with CF Fabric/PEI Prepreg

To estimate the possibility of using both low-melting TecaPEI and neat PEI films as energy directors (EDs) for ultrasonic welding (USW) of carbon fiber (CF) fabric–polyetherimide (PEI) laminates, some patterns of structure formation and mechanical properties of their lap joints were investigated by varying the process parameters. The experiment was planned by the Taguchi method with the L9 orthogonal matrix. Based on the obtained results, USW parameters were optimized accounting for maintaining the structural integrity of the joined components and improving their functional characteristics. The use of the low-melting EDTecaPEI film enabled US-welding the laminates with minimal damage to the fusion zone, and the achieved lap shear strength (LSS) values of ~7.6 MPa were low. The use of EDSolverPEI excluded thermal degradation of the components as well as damage to the fusion zone, and improved LSS values to 21 MPa. With the use of digital image correlation (DIC) and computed tomography (CT) techniques, the structural factors affecting the deformation behavior of the USW lap joints were justified. A scheme was proposed that established the relationship between structural factors and the deformation response of the USW lap joints under static tension. The TecaPEI film can be used in USW procedures when very high interlayer adhesion properties are not on demand

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

Effect of Prepreg Composition on the Structure and Shear Strength of PEI/CF Laminates Fabricated by Ultrasonic Additive Manufacturing

In this study, laminates based on polyetherimide (PEI) with contents of carbon fibers (CFs) from 55 to 70 wt.% were fabricated by thermoforming (TF) and ultrasonic additive manufacturing (UAM) methods. The UAM laminates with CF contents above 55 wt.% possessed shear strengths lower by 40% in comparison with those of the TF ones, due to insufficient amounts of the binder in the prepregs to form reliable interlaminar joints. For enhancing the shear strength of the laminates with a CF content of 70 wt.%. up to the levels of the TF ones, extra resin layers with thicknesses of 50, 100, and 150 μm were deposited. By ranking the UAM parameters using the Taguchi method, it was possible to increase the shear strengths by 30% as compared to those of the trial laminates. Further improvements were achieved by artificial neural network (ANN) modeling. As a result, the use of the 50 µm thick extra resin layer made it possible to increase the shear strengths up to 50% relative to those of the trial laminates at a CF content of 70 wt.%. This improvement was achieved via minimizing the number of defects at the interlaminar interfaces. The dependences of both mechanical and structural characteristics of the laminates on the UAM parameters were essentially nonlinear. For their analysis and optimization of the UAM parameters, the direct propagation neural networks with the minimal architecture were utilized. Under the ultra-small sample conditions, the use of a priori knowledge enabled us to predict the results rather accurately

Ultrasonic Welding of PEEK Plates with CF Fabric Reinforcement—The Optimization of the Process by Neural Network Simulation

The optimal mode for ultrasonic welding (USW) of the “PEEK–ED (PEEK)–prepreg (PEI impregnated CF fabric)–ED (PEEK)–PEEK” lap joint was determined by artificial neural network (ANN) simulation, based on the sample of the experimental data expanded with the expert data set. The experimental verification of the simulation results showed that mode 10 (t = 900 ms, P = 1.7 atm, τ = 2000 ms) ensured the high strength properties and preservation of the structural integrity of the carbon fiber fabric (CFF). Additionally, it showed that the “PEEK–CFF prepreg–PEEK” USW lap joint could be fabricated by the “multi-spot” USW method with the optimal mode 10, which can resist the load per cycle of 50 MPa (the bottom HCF level). The USW mode, determined by ANN simulation for the neat PEEK adherends, did not provide joining both particulate and laminated composite adherends with the CFF prepreg reinforcement. The USW lap joints could be formed when the USW durations (t) were significantly increased up to 1200 and 1600 ms, respectively. In this case, the elastic energy is transferred more efficiently to the welding zone through the upper adherend.</jats:p