Formulation Development Process Research Articles

Preparation and Characterization of Solid Dispersion of Sorafenib Tosylate for Enhancement of Bioavailability.

Solid dispersions have garnered significant attention as an effective strategy for enhancing the solubility and, consequently, the bioavailability of poorly water-soluble drugs. By forming solid dispersions of these drugs with water-soluble carriers, issues related to limited solubility have been mitigated, leading to improved dissolution rates. The core focus of solid dispersion technology revolves around the creation of two-component systems involving the drug and a polymer, where the successful dispersion and stabilization of the drug play pivotal roles in the formulation development process. As a result, this technology is widely acknowledged as a highly valuable approach for enhancing the dissolution properties of poorly water-soluble drugs. In recent years, a substantial body of knowledge has been amassed concerning solid dispersions. Nonetheless, their practical implementation in the commercial sphere remains somewhat limited. In the current investigation, we embarked on the preparation of a solid dispersion of sorafenib tosylate, with the primary objective of achieving the maximum release of the drug within the shortest possible timeframe. Our aim was to develop and optimize the solid dispersions of sorafenib tosylate in a manner that ensures the successful design and formulation of this system.

Development and Evaluation of Docetaxel-Loaded Nanostructured Lipid Carriers for Skin Cancer Therapy.

This study focuses on the design, characterization, and optimization of nanostructured lipid carriers (NLCs) loaded with docetaxel for the treatment of skin cancer. Employing a systematic formulation development process guided by Design of Experiments (DoE) principles, key parameters such as particle size, polydispersity index (PDI), zeta potential, and entrapment efficiency were optimized to ensure the stability and drug-loading efficacy of the NLCs. Combined XRD and cryo-TEM analysis were employed for NLC nanostructure evaluation, confirming the formation of well-defined nanostructures. In vitro kinetics studies demonstrated controlled and sustained docetaxel release over 48 h, emphasizing the potential for prolonged therapeutic effects. Cytotoxicity assays on human umbilical vein endothelial cells (HUVEC) and SK-MEL-24 melanoma cell line revealed enhanced efficacy against cancer cells, with significant selective cytotoxicity and minimal impact on normal cells. This multidimensional approach, encompassing formulation optimization and comprehensive characterization, positions the docetaxel-loaded NLCs as promising candidates for advanced skin cancer therapy. The findings underscore the potential translational impact of these nanocarriers, paving the way for future preclinical investigations and clinical applications in skin cancer treatment.

Quantifying the bitter masking effect of drug-cyclodextrin complexation: NMR-ROESY mixing time approach

Taste, especially unpleasant taste, can be key for patient compliance. In the formulation development process, drug-cyclodextrin (CD) inclusion complexes are often used to improve the solubility of a drug and/or mask its bitterness. This study aimed to evaluate the bitter masking effect of CDs on different drugs using NMR-ROESY analysis, human sensory tests, and e-tongue measurements. The strength of inclusion complex formation between drugs and CDs was investigated by NMR-ROSEY, and these results were compared to human sensory test results. In the sensory test, participants identified which drug-CD inclusion complexes were not bitter. NMR-ROSEY results aligned with the sensory tests; short magnetization transfer times corresponded to masked bitterness. The electrical tongue was not able to detect the taste of any of the drug-CD inclusion complexes. Additionally, we used NMR-ROSEY to determine which drug-CD inclusion complex formed in a system with multiple drug substances present. This research offers valuable insights into the bitter masking effect of CDs on different drugs and presents a comprehensive evaluation approach using various methods. This knowledge has significant implications for the pharmaceutical industry, clinical practice, and patient care, contributing to improved patient compliance and satisfaction with bitter medications.

Reliable particle sizing in vaccine formulations using advanced dynamic light scattering

Understanding the impact of lipid nanoparticle size on immunogenicity represents an important step for enabling the rapid development of novel vaccines against known or emergent diseases. Dynamic light scattering, also known as quasi-elastic light scattering or photon correlation spectroscopy, has established itself as an optimal analytical method to determine particle size due to its in-situ approach and fast measurements. However, its application to many systems of industrial relevance has been hindered due to artifacts arising from multiple scattering. Result interpretation becomes severely compromised depending on the concentration of the system and the size of the particles. In this context, strong sample dilution is often required, bringing additional uncertainties to the formulation development process. Here, we show how advanced dynamic light scattering technology can filter out multiple scattering from the signal and yield fully accurate sizing measurements regardless of the sample concentration. We illustrate this in a comparative study with standard dynamic light scattering using polystyrene beads as model suspension as well as a concentrated commercial lipid nanoparticle adjuvant (AddaVax™).

Physical model-based ArF photoresist formulation development

Since the logic 28 nm technology and beyond, ArF immersion lithography has been widely used in manufacturing. To fully utilize the potential of the lithographic resolution, process simulation has been used since the lithography process setup step. The accuracy of the model prediction can be a key factor in the process stability, defectivity, and yield of the final product. To get better model prediction, it is very important to develop a process model with as many parameters as possible with physical meanings. Since the analysis of exposure data with a physical model can provide insights into the process and the photoresist material, it may greatly accelerate the photoresist formulation development or improvement process. From another aspect, as the function of the photoresist is to record the aerial image information on wafer, theoretically, the photoresist image should not deviate much from the aerial image or it will inevitably reduce the information content of the aerial image, i.e., through various kinds of averaging, mixing, etc. Since the emergence of Optical Proximity Correction (OPC), the time that takes to finalize a photolithography process has been significantly lengthened and made more complicated. To make the OPC model more reliable and less dependent on patches, a more physical OPC model is necessary. Therefore, it is clear that if we can develop a photoresist formulation to realize a more physical photoresist image, or a photoresist image closer to the aerial image, we can greatly improve the photolithography process performance and the time consumed for the process setup including OPC. In this paper, we focus on several physical model parameters for photoresist, especially the effective photoacid diffusion length and photo decomposable base parameters. We will show that these two parameters can provide a good description of photoresist imaging behavior, and the resist formulation can be improved to match the physical model prediction with more accuracy.

Divorce prediction using machine learning algorithms in Ha’il region, KSA

The application of artificial intelligence (AI) in predictive analytics is growing in popularity. It has the power to offer ground-breaking solutions for a range of social problems and real world societal difficulties. It is helpful in addressing some of the social issues that today’s world seems incapable of solving. One of the most significant phenomena affecting people’s lives is divorce. The goal of this paper is to study the use of machine learning algorithms to determine the effectiveness of divorce predictor scale (DPS) and identify the reasons that usually lead to divorce in the scenario of Hail region, KSA. For this purpose, in this study, the DPS, based on Gottman couples therapy, was used to predict divorce by applying different machine learning algorithms. There were 54 items of the DPS used as features or attributes for data collection. In addition to the DPS, a personal information form was utilized to gather participants’ personal data in order to conduct this study in a more structured and traditional manner. Out of 148 participants 116 participants were married whereas 32 were divorced. With the use of algorithms artificial neural network (ANN), naïve bayes (NB), and random forest (RF), the effectiveness of DPS was examined in this study. The correlation based feature selection method was used to identify the top six features from the same dataset and the highest accuracy rate was 91.66% with RF. The results show that DPS can predict divorce. This scale can help family counselors and therapists in case formulation and intervention plan development process. Additionally, it may be argued that the Hail region, KSA sampling confirmed the Gottman couples treatment predictors.

Formulation Development and Evaluation of Highly Oxidative Degradative Drug Molecule Injectable Dosage form by Lyophilisation Techniques

Adrenaline (ADR) It is lifesaving and is the only first-line drug for the treatment of anaphylaxis. Adrenaline (ADR) is the endogenous catecholamine with potent alpha- and beta-adrenergic stimulating properties. Alpha-adrenergic action increases systemic and pulmonary vascular resistance, increasing both systolic and diastolic blood pressure. Adrenaline is released in the bloodstream, which increases heartbeat, muscle strength, blood pressure, and glucose metabolism. Oxidation degradation of the medicinal product is the main route of degradation. Affects chemical and physical changes in drugs during the formulation development process. Physicochemical changes in the product can affect both the safety and efficacy of the drug product. Main product development strategy to develop, a stable, freeze-dried product of epinephrine for injection. The stability of adrenaline injection is of paramount objective as it is classified as a catechol compound that is sensitive to oxidation to o-quinone and therefore can react further to form highly colored compounds. Adrenergic drugs further react to form adrenochrome, a highly colored indole derivative. The rate of this reaction increased with pH, temperature and presence of the metal ions. Aqueous solutions of adrenergic agonists decompose rapidly when exposed to air, light, or heat, turning pink due to oxidation to adrenochrome and brown due to melanin formation. Due to its strong oxidizing properties and easy decomposition in aqueous solutions. To achieve this, the aqueous solutions of adrenergic agonists decompose rapidly when exposed to air, light, or heat, turning pink due to oxidation to adrenochrome and brown due to melanin formation. Due to its strong oxidizing properties and easy decomposition in aqueous solutions.

A dataset of formulation compositions for self-emulsifying drug delivery systems

Self-emulsifying drug delivery systems (SEDDS) are a well-established formulation strategy for improving the oral bioavailability of poorly water-soluble drugs. Traditional development of these formulations relies heavily on empirical observation to assess drug and excipient compatibility, as well as to select and optimize the formulation compositions. The aim of this work was to leverage previously developed SEDDS in the literature to construct a comprehensive SEDDS dataset that can be used to gain insights and advance data-driven approaches to formulation development. A dataset comprised of 668 unique SEDDS formulations encompassing 20 poorly water-soluble drugs was curated. While there are still opportunities to enhance the quality and quantity of data on SEDDS, this research lays the groundwork to potentially simplify the SEDDS formulation development process.

The distribution of Drucker-Prager Cap model parameters for pharmaceutical materials

A model of the compaction process that has come into increasing use with the pharmaceutical industry is the Drucker-Prager Cap model (DPC). It has long been used in conjunction with the finite element methods to simulate the density and stress distributions in compacts. More recently the calibrated parameters that make up the model have been used to characterize the material properties of many compounds. This work will summarize the findings from the collection of DPC parameters from 60 materials. From this dataset several important conclusions can be drawn about typical and extreme behaviors of formulations and excipients. From these findings key signals of program risk can be determined and utilized early in the formulation development process to avoid costly manufacturing and scale-up issues.

Study of glycopyrrolate dry powder behavior in new single dose inhalers under generic product development

With increasing environmental pollution and tendency to smoking, the number of patients suffering from chronic obstructive pulmonary disease is increasing and a demand for generic products for the treatment of chronic obstructive pulmonary disease (COPD) is growing. The long process of generic formulation development can be accelerated by using an inhaler with suitable fluid dynamics features. This paper presents a study of a newly patented single-dose Memmatec Mora® inhaler in two versions: with grid openings 1.0 (A) and 0.5 mm (B), using Seebri Breezhaler® as a reference product. The studies were conducted with inhalation powders containing 44μg glycopyrronium bromide placed in a capsule. The manuscript presents studies during the selection of a generic inhaler within the course of inhalation powder development by a pharmaceutical company. The fluid dynamics features of the inhalers and the dose delivered were determined. The aerodynamic particle size distribution (APSD) and the inhalable dose were tested using a cascade impactor. The delivery dose for the Breezhaler, Mora A and Mora B inhalers for a volumetric flow of 100 L×min-1 was 38.5, 40.5 40.3μg, respectively. Fine particle fraction of the inhalers tested was 22.3, 24.6 25.5μg, respectively. Results of the study indicate that increase of the glycopyrronium bromide inhalation dose can be achieved not only by changes in the manufacturing technology of inhalation powder, hard capsule technology or addition of best quality raw materials, but also by the use of a properly selected inhaler with specific fluid dynamics features.

Graft Copolymers of Polysaccharide: Synthesis Methodology and Biomedical Applications in Tissue Engineering.

A polymer is a macromolecule that has a significant number of repeating units. It is possible to modify the architecture of a polymer via grafting, bridging, mixing, or generating composites. There are several uses for using natural polymers in culinary and medicinal applications. Polymeric materials became appealing because of their low density and ability to incorporate properties of their constituent constituents. High-energy accelerated electrons from the plasma induce chemical bond breaking in the polymeric structure, resulting in the generation of macromolecule radicals and graft copolymerization. Polymer grafting has become an important aspect of the formulation development process. When polymer functional groups are changed, a wide variety of desirable and unwanted properties can be added or removed. It can be concluded from the findings of the literature survey that graft copolymers of polysaccharides have significant biomedical applications including drug delivery and tissue engineering applications.

Anabolic Peptide-Enriched Stealth Nanoliposomes for Effective Anti-Osteoporotic Therapy.

The objective of the present work was to develop PTH (1-34)-loaded stealth nanoliposomes (PTH-LPs) by employing the use of the Quality by Design (QbD) approach. Risk identification was carried out using the Ishikawa fishbone diagram. PTH-LPs were optimized using Box Behnken Design, a type of response surface methodology to examine the effect of independent variables on dependent variables such as particle size and percentage entrapment efficiency (%EE). Design space was generated for PTH-LPs to reduce interbatch variability during the formulation development process. Furthermore, a cytotoxicity assay, cell proliferation assay, calcium calorimetric assay, mineralized nodule formation, and cellular uptake assay were carried out on MG-63 osteoblast-like cells. The results obtained from these procedures demonstrated that lipid concentration had a significant positive impact on particle size and %EE, whereas cholesterol concentration showed a reduction in %EE. The particle size and %EE of optimized formulation were found to be 147.76 ± 2.14 nm and 69.18 ± 3.62%, respectively. Optimized PTH-LPs showed the sustained release profile of the drug. In vitro cell evaluation studies showed PTH-LPs have good biocompatibility with MG-63 cells. The cell proliferation study revealed that PTH-LPs induced osteoblast differentiation which improved the formation of mineralized nodules in MG-63 cells. The outcome of the present study conclusively demonstrated the potential of the QbD concept to build quality in PTH-LPs with improved osteoanabolic therapy in osteoporosis.

The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology

Dry powder inhalers (DPIs) are one of the most widely used devices for treating respiratory diseases. Thin--film--freezing (TFF) is a particle engineering technology that has been demonstrated to prepare dry powder for inhalation with enhanced physicochemical properties. Aerosol performance, which is indicated by fine particle fraction (FPF) and mass median aerodynamic diameter (MMAD), is an important consideration during the product development process. However, the conventional approach for formulation development requires many trial-and-error experiments, which is both laborious and time consuming. As a state-of-the art technique, machine learning has gained more attention in pharmaceutical science and has been widely applied in different settings. In this study, we have successfully built a prediction model for aerosol performance by using both tabular data and scanning electron microscopy (SEM) images. TFF technology was used to prepare 134 dry powder formulations which were collected as a tabular dataset. After testing many machine learning models, we determined that the Random Forest (RF) model was best for FPF prediction with a mean absolute error of ± 7.251%, and artificial neural networks (ANNs) performed the best in estimating MMAD with a mean absolute error of ± 0.393 μm. In addition, a convolutional neural network was employed for SEM image classification and has demonstrated high accuracy (>83.86%) and adaptability in predicting 316 SEM images of three different drug formulations. In conclusion, the machine learning models using both tabular data and image classification were successfully established to evaluate the aerosol performance of dry powder for inhalation. These machine learning models facilitate the product development process of dry powder for inhalation manufactured by TFF technology and have the potential to significantly reduce the product development workload. The machine learning methodology can also be applied to other formulation design and development processes in the future.

Accelerating 3D printing of pharmaceutical products using machine learning

Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application, M3DISEEN, that provides predictions on filament characteristics, printability, HME and FDM processing temperatures, and drug release profiles (https://m3diseen.com/predictionsFDM/). By simulating the workflow of preparing FDM-printed pharmaceutical products, the web-application expedites the otherwise empirical process of formulation development, facilitating higher pharmaceutical 3DP research throughput.

Microemulsion interface model for chemical enhanced oil recovery design

The surfactant phase behavior laboratory test for chemical enhanced oil recovery (EOR) formulation design is time consuming. However, it is possible to use computational chemistry simulation to minimize the duration. The only known non-empirical approach to predict surfactant phase behavior is by surface tension analysis, but the optimum phase behavior boundary is unclear and its applicability in actual complex crude oil is unproven. This research overcomes these issues by developing a microemulsion interface model using digital oil model with accurate representation of atomistic components of actual crude oil as inputs to the simulation. The microemulsion interface model is developed based on physical chemistry of surface tension and torque concepts coupled with solution of interface bending rigidity in relation to surfactant solubilization and interface energy. The model is implemented in coarse-grained molecular dynamics simulation technique. The microemulsion interface model is verified with surfactant phase behavior laboratory data using actual crude oil. Good agreement for 12 out of 14 chemical EOR formulations between simulations and phase behavior laboratory results is achieved. This indicates that the main characteristics and physics of the formation of optimal microemulsion were captured correctly in the microemulsion interface model. The duration for surfactant phase behavior determination can be reduced from 14 days in laboratory down to 1.5 day by using the microemulsion interface model, resulting in 90%-time reduction. This faster and more informed formulation development process can minimize time and costly resources as chemical EOR formulations proceed into field implementation.

Investigation of Chloroquine Resinate Feasibility and In Vitro Taste Masking Evaluation for Pediatric Formulations.

In this study, chloroquine resinates were prepared at a 1:1 (w:w) drug-to-resin ratio using the batch method with polacrilex (PC), sodium polystyrene sulfonate (SPS), and polacrilin potassium (PP) ion exchange resins (IER). The influence of drug/resin ratio and pH of the medium on drug loading efficiency was explored. UV-VIS spectrophotometric analysis showed that SPS resin had high loading efficiency for chloroquine diphosphate (CLP), above 89%, regardless of the pH. PP resin was more effective at pH 5.0 (90.68%) than at pH 1.0 (2.09%), and PC resin had only 27.63% of CLP loading efficiency. CLP complexation with IER yielded amorphous mixtures according to results from differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD), thus indicating drug-resin interaction. The taste masking efficiency was evaluated with in vitro methods using an adapted dissolution test and an electronic tongue system. During dissolution tests, SPS released only 1.0% of CLP after 300 s, while PP released over 10% after 90 s in simulated saliva solution. The electronic tongue distinguished the samples containing CLP, resins, and resinates by using multidimensional projection techniques that indicated an effective drug taste masking. In an accelerated stability study, the drug contents did not decrease in chloroquine resinates, and there was no physical degradation of the resinates after 60 days. Using chloroquine resinates therefore represents a novel way to evaluate taste masking in vitro which is relevant for the early formulation development process.

Optimization of diluents on the basis of SeDeM-ODT expert system for formulation development of ODTs of glimepiride

The present study aimed to optimize diluents characteristics (rheological characteristics, compressibility and disintegration behavior) using SeDeM-ODT expert system, to mask poor characteristics of APIs and will help in development of a formulation which is to be processed by direct compression technique. On the basis of SeDeM-ODT experts system, various parameters were determined for glimepiride, diluents and other excipients, and evaluated their suitability for direct compression and bucco-dispersibility. Diluents were selected and powder blend of all the developed formulations were tested as per SeDeM-ODT expert system. Powder blends were compressed to ODTs and cross carmellose sodium was used as super dissintegrant. Different parameters of the powder blend related to flow were evaluated while compressed tablets were subjected to various official and un-official quality control tests. SeDeM-ODT Experts system was successfully applied for optimization of diluents and all the formulations exhibited better flow, disintegration behavior and sufficient mechanical strength, irrespective of the addition of other excipients having poor characteristics. Results of the developed formulations was same as predicted in terms of rapid disintegration (disintegration < 60sec) and high mechanical strength (crushing strength greater than 40 N) of the tablets. SeDeM-ODT expert system can help in selection of diluents with optimum characteristics, required for tablet preparation by direct compression, facilitating the process of formulation development and avoided extensive experimentation, as carried out in conventional statistical optimization.

Validation of spectrophotometric method to quantify cabotegravir in simulated vaginal fluid and porcine vaginal tissue in ex vivo permeation and retention studies from thermosensitive and mucoadhesive gels

Cabotegravir (CAB) is an antiretroviral therapy (ARV) used for Human Immunodeficiency Virus (HIV) treatment. CAB has low solubility, which affects its bioavailability in oral therapy. Moreover, the injection form of CAB has difficulty in the administration process. Therefore, it is essential to develop a new drug delivery system for CAB. Vaginal drug delivery system offers many advantages such as a large surface area, increased drug bioavailability, and improved drug delivery. CAB was developed in thermosensitive and mucoadhesive vaginal gel preparations that provided optimal distribution in the vaginal mucosa. To support the process of formulation development, in this study, UV–visible spectrophotometry method was validated in methanol, simulated vaginal fluid (SVF) and vaginal tissue to quantify the amount of CAB in the gel preparations, in vitro, and ex vivo studies, respectively. The developed analytical method was subsequently validated according to ICH guidelines. The calibration curves in these matrices were found to be linear with correlation coefficient values (R2) ≥ 0.998. The LLOQ values in methanol, SVF and vaginal tissue were 2.15 µg/mL, 2.22 µg/mL, and 5.13 µg/mL, respectively. The developed method was found to be accurate and precise without being affected by dilution integrity. These methods were successfully applied to quantify the amount of CAB in gel preparations, in vitro, and ex vivo studies, showing uniformity of drug content and controlled release manner in the permeation profile for 24 h for both thermosensitive and mucoadhesive vaginal gels. Further analytical method is required to be developed for the quantification of CAB in in vivo studies.

Non-Pressurized Topical Spray Pharmaceutical-Methodology of Formulation Development and Quality Control Management

The medicine was given to the target site with a pressurized topical spray (aerosol pharmaceutical) and a non-pressurized topical spray solution by gently pressing the spray container against the skin and initiating the spray. Formulation components consists of product concentrate (one or more pharmaceutical drugs with relevant excipient) and packaging components (valves, pump, actuator, container). After formulation process development various quality control test like spray pattern, spray content uniformity and geometry, particulate matter, droplet size distribution, microbial limit, net content, pH, osmolarity, weight loss, leachability, viscosity, valves, actuators, diptubes, weight checking, leak test, valve discharge rate, dosage with valves, therapeutic activity, and toxicity studies to check the quality and efficacy. The benefit of Topical Spray Life Cycle Management (post-approval adjustments, 30-day supplements, annual report, Food and Drug Administration inspection and enforcement, cover letter, risk management standard and related information from the International Organization for Standardization) is that it covers the entire lifecycle of a topical spray, from conception through formulation design development to marketing. The product lifecycle management data offers regulatory bodies with a significant financial benefit. Topical non-pressurized sprays have been developed for a variety of applications due to their ease of use, environmental friendliness, low cost, and innovative technology. This review give details about meter dose topical spray of Estradiol, 1% tolnaftate, 2% Miconazole nitrate spray, 0.5% w/w Voriconazole spray, Clotrimazole transdermal apray, Lopinavir meter dose transdermal spray are studied by researcher. This review also enlisted patents and marketed formulation on topical spray from year 2007 to 2020.

Considerations for Buffering Agent Selection for Frozen rAAV2 Mediated Gene Therapy Products.

The buffering component selection is a key criterion for the formulation development process for biopharmaceuticals. This decision for recombinant adeno-associated virus (rAAV) mediated gene therapies is receiving special attention due to their rise in clinical trials which may require high concentration, frozen supply chain, and direct delivery to eye and central nervous system related sites. In the present study, we investigate the impact of rates of freezing and thawing on rAAV2 as a model serotype. It was observed that slow rate of thawing impacts rAAV2 colloidal stability in Phosphate based buffering system. Our pre-formulation workflow suggests that rAAV2 has maximum aggregation propensity between pH of 5.5 to 6.5. Thus, the overlap of maximum aggregation propensity pH range with acidic pH shift in Phosphate based buffering system during freezing and thawing appears to be responsible for 42-75% concentration drop noticed for rAAV2. This impact appears to be fully mitigated upon replacement of Phosphate based buffering system with an alternate buffer system such as Tris. The results reported in this study highlight associated risks and provide preliminary guidance on handling of early stage frozen rAAV mediated gene therapies.