Advancing injection force modeling and viscosity-dependent injectability evaluation for prefilled syringes.

The development of PFS requires a detailed understanding of the forces occurring during the drug administration process and patient's capability. This research describes an advanced mathematic injection force model that consisting hydrodynamic force and friction force. The hydrodynamic force follows the basic law of Hagen-Poiseuille but refines the modeling approach by delving into specific properties of drug viscosity (Newtonian and Shear-thinning) and syringe shape constant, while the friction force was accounted from empty barrel injection force. Additionally, we take actual temperature of injection into consideration, providing more accurate predication. The results show that the derivation of the needle dimension constant and the rheological behavior of the protein solutions are critical parameters. Also, the counter pressure generated by the tissue has been considered in actual administration to address the issue of the inaccuracies of current injection force evaluation preformed in air, especially when the viscosity of the injected drug solution is below 9.0 cP (injecting with 1 mL L PFS staked with 29G ½ inch needle). Human factor studies on patients' capability against medication viscosity filled the gap in design space of PFS drug product and available viscosity data in very early phase.

Key Process Parameters Study for the Fill Finish of Vaccines Containing Aluminum Hydroxide Adjuvant.

Vaccine manufacturing is one of the most challenging and complex processes in pharmaceutical industry, and the process control strategy is critical for the safety, effectiveness, and consistency of a vaccine. The efficacy of aluminum salt adjuvant on vaccines strongly depends on its physicochemical properties, such as size, structure, surface charge, etc. However, stresses during the vaccine manufacturing may affect the stability of adjuvant. In this study, the impacts of cold/thermal stress, autoclaving, pumping, mixing, and filling shear stress on the physicochemical properties of aluminum hydroxide (AH) adjuvant were evaluated as part of the manufacturing process development. The results showed that the autoclaving process would slightly influence the structure and properties of the investigated AH adjuvant, but thermal incubation at 2-8 °C, 25 °C and 40 °C for 4 weeks did not. However, -20 °C freezing AH adjuvant led to the adjuvant agglomeration and rapid sedimentation. For the high shear stress study with mixing at 500 rpm in a 1-L mixing bag and pumping at 220 rpm for up to 24 h, the average particle dimension of the bulk AH adjuvant decreased, along with decreasing protein adsorption ratio. The studies indicate that various stresses during manufacturing process could affect the structure and physicochemical properties of AH adjuvant, which calls for more attention on the control of adjuvant process parameters during manufacturing.

A Review of Recent FDA-Approved Biologic-Device Combination Products.

With the remarkably strong growth of the biopharmaceutical market, an increasing demand for self-administration and rising competitions attract substantial interest to the biologic-device combination products. The ease-of-use of biologic-device combination products can minimize dosing error, improve patient compliance and add value to the life-cycle management of biological products. As listed in the purple book issued by the U.S. Food and Drug Administration (FDA), a total of 98 brand biologic-device combination products have been approved with Biologic License Application from January 2000 to August 2023, where this review mainly focused on 63 products containing neither insulin nor vaccine. Prefilled syringes (PFS) and autoinjectors are the most widely adopted devices, whereas innovative modifications like needle safety guard and dual-chamber design and novel devices like on-body injector also emerged as promising presentations. All 16 insulin products employ pen injectors, while all 19 vaccine products are delivered by a PFS. This review provides a systematic summary of FDA-approved biologic-device combination products regarding their device configurations, routes of administration, formulations, instructions for use, etc. In addition, challenges and opportunities associated with biologic-device compatibility, regulatory complexity, and smart connected devices are also discussed. It is believed that evolving technologies will definitely move the boundaries of biologic-device combination product development even further.

Method of depth simulation imaging and depth image super-resolution reconstruction for a 2D/3D compatible CMOS image sensor.

This paper presents a depth simulation imaging and depth image super-resolution (SR) method for two-dimensional/three-dimensional compatible CMOS image sensors. A depth perception model is established to analyze the effects of depth imaging parameters and evaluate the real imaging effects. We verify its validity by analyzing the depth error, imaging simulation, and auxiliary physical verification. By means of the depth simulation images, we then propose a depth SR reconstruction algorithm to recover the low-resolution depth maps to the high-resolution depth maps in two types of datasets. With the best situation in depth accuracy kept, the root mean square error (RMSE) of Middlebury dataset images are 0.0156, 0.0179, and 0.0183 m. The RMSE of RGB-D dataset images are 0.0223 and 0.0229 m. Compared with other listed conventional algorithms, our algorithm reduces the RMSE by more than 16.35%, 17.19%, and 23.90% in the Middlebury dataset images. Besides, our algorithm reduces the RMSE by more than 9.71% and 8.76% in the RGB-D dataset images. The recovery effects achieve optimized results.

Ambient light rejection method for multiphoton coincidence detection in single-photon avalanche diode-based DToF sensors.

This paper presents an adaptive control method used for multiphoton coincidence detection to reduce the effect of ambient light that exists in accessing flight time. Behavioral and statistical models are used to demonstrate the working principle with MATLAB, and the method is achieved through a compact circuit. The adaptive coincidence detection in accessing flight time achieves a higher probability of 66.5% than fixed parameter coincidence detection's 46%, while ambient light intensity is 75 klux. Additionally, it also can achieve a dynamic detection range 43.8 times higher than the fixed parameter detection. The circuit is designed in 0.11 µm complementary metal-oxide semiconductor process, and the area consumption is 0.00178m m 2. The postsimulation experiment through Virtuoso shows that the histogram of coincidence detection under adaptive control circuit is consistent with the behavioral model. The proposed method acieves the coefficient of variance as 0.0495 smaller than fixed parameter coincidence's 0.0853, which means better ambient light tolerance in accessing flight time for three-dimensional imaging.

Correlation between the Protein Pharmaceutical Surface Activity and Interfacial Stability.

The interaction of protein drugs with the air-liquid interface plays a crucial role in the overall stability in aqueous formulations, particularly when the adsorbed proteins are subjected to the surface flow. Nonionic surfactants are usually added into the formulation solutions to address this issue. A diversity of studies have been focused on the usage of surfactants, the stability mechanism of surfactants, or seeking new pharmaceutical surfactants. However, the real protagonist, the basic properties of protein drugs, was neglected, which may play a vital role in the stability of protein drugs. Herein, we aim to clarify the correlation between the surface behavior of proteins and the interfacial stability. A force tensiometer is used to track the surface tension reduction and the competition between surfactants and proteins at the surface. We find that the surface behaviors of proteins vary with storage temperature and protein types including monoclonal antibodies (mAb), bispecific monoclonal antibodies (BsAb), and antibody-drug conjugates (ADCs). Especially for the protein stored at 5 °C, the surface activity of proteins is better than that of surfactants. It indicates that the ability of proteins to adsorb at the interface should not be ignored compared to surfactants. The significant difference in the interfacial stability of protein pharmaceuticals formulated in the same buffer and excipients as well as the surfactants with the same concentration further confirms the interfacial adsorption capacity of proteins that should not be ignored. These findings provide a new angle and valuable insights into the correlation between the surface activity of the proteins and interfacial stability, which may pave the way for future preformulation studies on therapeutic proteins and broaden the thoughts of formulation development.

Method for power reduction of demodulation driver circuit in indirect time-of-flight CMOS image sensor.

To improve the depth accuracy of an indirect time-of-flight CMOS image sensor, high modulation frequency is often adopted. It will result in high power consumption of an on-chip demodulation driver, and this problem will be much more serious when the resolution of the sensor is much higher. In this paper, a power reduction method that can lower the power consumption of the demodulation driver circuit during the integration time while obtaining accurate high-resolution depth maps is proposed and analyzed theoretically. The method decreases the number of driven pixels at a high-modulation frequency by a programmable resolution adjustment circuit to obtain an accurate low-resolution depth map. A low-depth accuracy high-resolution depth map is obtained at a low modulation frequency, and then a modified super-resolution algorithm is used to obtain an accurate high resolution solution depth map. To demonstrate the effectiveness of the proposed method, a model is established based on the actual indirect time-of-flight sensor architecture, then the depth error and power consumption are analyzed by the simulation results of the model. In the simulation, 25 MHz and 100 MHz are used as the low modulation frequency and high modulation frequency, respectively. With the best scenario in depth accuracy kept, average power consumption decreases 38.47% and peak power consumption decreases 49.83% while the depth error that is represented by RMSE merely increases 8.08%.

Computational fluid dynamics (CFD) insights into agitation stress methods in biopharmaceutical development.

Agitation of small amounts of liquid is performed routinely in biopharmaceutical process, formulation, and packaging development. Protein degradation commonly results from agitation, but the specific stress responsible or degradation mechanism is usually not well understood. Characterization of the agitation stress methods is critical to identifying protein degradation mechanisms or specific sensitivities. In this study, computational fluid dynamics (CFD) was used to model agitation of 1 mL of fluid by four types of common laboratory agitation instruments, including a rotator, orbital shaker, magnetic stirrer and vortex mixer. Fluid stresses in the bulk liquid and near interfaces were identified, quantified and compared. The vortex mixer provides the most intense stresses overall, while the stir bar system presented locally intense shear proximal to the hydrophobic stir bar surface. The rotator provides gentler fluid stresses, but the air-water interfacial area and surface stresses are relatively high given its low rotational frequency. The orbital shaker provides intermediate-level stresses but with the advantage of a large stable platform for consistent vial-to-vial homogeneity. Selection of experimental agitation methods with targeted types and intensities of stresses can facilitate better understanding of protein degradation mechanisms and predictability for "real world" applications.

Prediction of dissolution profiles of acetaminophen beads using artificial neural networks.

Immediate release acetaminophen (APAP) beads with 40% drug loading were prepared using the extrusion-spheronization process. Eighteen batches of beads were prepared based on a full factorial design by varying process variables such as extruder type, extruder screw speed, spheronization speed, and spheronization time. An in vitro dissolution test was carried out using the USP 27 Apparatus II (paddle) method. Artificial Neural Network (ANN) models were developed based on the aforementioned process variables and dissolution data. The trained ANN models were used to predict the dissolution profiles of APAP from the beads, which were prepared with various processing conditions. For training the ANN models, process variables were used as inputs, and percent drug released from APAP beads was used as the output. The dissolution data from one out of 18 batches of APAP beads was selected as the validation data set. The dissolution data of other 17 batches were used to train the ANN models using the ANN software (AI Trilogy) with two different training strategies, namely, neural and genetic. The validation results showed that the ANN model trained with the genetic strategy had better predictability than the one trained with the neural strategy. The ANN model trained with the genetic strategy was then used to predict the drug release profiles of two new batches of APAP beads, which were prepared with process variables that were not used during the ANN model training process. However, the process variables used to prepare the two new batches of APAP beads were within the confines of the process variables used to prepare the 18 batches. The actual drug release profile of these two batches of APAP beads was similar to the ones predicted by the trained and validated ANN model, as indicated by the high f2 values. Furthermore, the ANN model trained with genetic strategy was also used to optimize process variables to achieve the desired dissolution profiles. These batches of APAP beads were then actually prepared using the process variables predicted by the trained and validated ANN model. The dissolution results showed that the actual dissolution profiles of the APAP beads prepared from the predicted process variables were similar to the desired dissolution profiles.