Formulation and evaluation of polymeric micelles for improved oral delivery of tenofovir disoproxil fumarate and zidovudine using poly-lactic-co-glycolic acid nanoparticles

Magister Pharmaceuticae - MPharmBackground: Tenofovir disoproxil fumarate (TDF) and Zidovudine (AZT) are both nucleotide and nucleoside analogue reverse transcriptase inhibitors (NtRTIs and NRTIs), respectively. They are used for the management and prevention of the Human Immunodeficiency Virus (HIV) infection. These drugs are faced with oral delivery challenges such as low intestinal permeability and extensive first pass liver metabolism for TDF and AZT, respectively. Their use may also be limited by dose-dependent adverse effects, which may result in treatment failure when patients become non-compliant and non-adherent to their prescribed antiretroviral (ARV) regimen. Non-compliance and non-adherence to ARV regimen may lead to drug resistance and a need for change in regimen, which can be very expensive, not only financially but in terms of morbidity and mortality. To solve such issues, a new drug can be formulated, or an existing drug can be modified. The development and formulation of a new drug is time consuming and expensive, especially with no available data and a high probability of failure. Modifying existing drugs is a cheaper, less time-consuming option with lower probability of failure. Such modification can be achieved via non-covalent interactions using various methods such as preparation of nano-particulates with polymeric micelles (a non-covalent interaction). Polymeric micelles offer a variety of polymers to choose from for drug modification purposes. Purpose: The aim of this study was to formulate polymeric nanoparticles of TDF and AZT using different ratios of poly-lactic-co-glycolic acid (PLGA), characterize the formulated nanoparticles (using the following analyses: particle size, zeta potential, encapsulation efficiency, hot stage microscopy, thermogravimetric analysis, differential scanning calorimetry, Fourier transform infrared spectroscopy and scanning electron microscopy), analyze for stability during storage (2-8˚C) and determine the release rate of the active pharmaceutical ingredients in the formulated nanoparticles. Methods: Nanoparticles were prepared using a modified version of the double emulsion (water-in-oil-in-water) solvent evaporation and diffusion method. Two ratios of PLGA (50:50 and 85:15) were used to prepare four formulations (two each of TDF and AZT). Thereafter, the physicochemical and pharmaceutical properties of the formulations were assessed by characterizing the nanoparticles for particle size, zeta potential, polydispersity index, percentage yield, release profile and particle morphology, using the suggested analytical techniques. Results: For TDF-PLGA 85:15, TDF-PLGA 50:50, AZT-PLGA 85:15 and AZT-PLGA 50:50, nanoparticles of 160.4±1.7 nm,154.3±3.1 nm,127.0±2.32 nm and 153.2±4.3 nm, respectively, were recovered after washing. The polydispersity index (PDI) values were ≤0.418±0.004 after washing, indicating that the formulations were monodispersed. The zeta potential of the particles was -5.72±1 mV, -19.1 mV, -12.2±0.6 mV and -15.3±0.5 mV for TDF-PLGA 85:15, TDF-PLGA 50:50, AZT-PLGA 85:15 and AZT-PLGA 50:50 respectively after washing. The highest percentage yield was calculated to be 79.14% and the highest encapsulation efficiency obtained was 73.82% for AZT-PLGA 50:50, while the particle morphology showed spherical nanoparticles with signs of coalescence and aggregation for all formulated nanoparticles. The release profiles were biphasic; that is, an initial burst which indicated the presence of surface API followed by sustained release. Comparing the release profiles of AZT and TDF at pH 1.2 and 7.4, it was indicative that more AZT was released at pH 1.2 while more TDF was released at pH 7.4. On computing the release data further into various mathematical models, the Weibull model was found to be the best fit. The loaded nanoparticles showed an increase in stability after washing; however, they showed signs of gradual decrease in stability after 10 days of storage at 2-8°C. Conclusions: Relatively small, spherical and smooth nanoparticles were formulated. The nanoparticle release profile was indicative of sustained release; however, there was no conclusive indication that 48 hours duration was sufficient to release all encapsulated drug. Further studies with an increased API or polymer ratio in the formulation needs to be performed to determine if the encapsulation efficiency can be improved and in-vivo studies are required for a better understanding of the API release from formulations as well as its absorption in the body

Milk progesterone measures to improve genomic selection for fertility in dairy cows

Improved reproductive performance has a substantial benefit for the overall profitability of dairy cattle farming by decreasing insemination and veterinary treatment costs, shortening calving intervals, and lowering the rate of involuntary culling. Unfortunately, the low heritability of classical fertility traits derived from calving and insemination data makes genetic improvement by traditional animal breeding slow. Therefore, there is an interest in finding novel measures of fertility that have a higher heritability or using genomic information to aid genetic selection for fertility. The overall objective of this thesis was to explore the use of milk progesterone (P4) records and genomic information to improve selection for fertility in dairy cows. In a first step, the use of in-line milk progesterone records to define endocrine fertility traits was investigated, and genetic parameters estimated. Several defined endocrine fertility traits were heritable, and showed a reasonable repeatability. Also, the genetic correlation of milk production traits with endocrine fertility traits were considerably lower than the correlations of milk production with classical fertility traits. In the next step 17 quantitative trait loci (QTL) associated with endocrine fertility traits, were identified on Bos taurus autosomes (BTA) 2, 3, 8, 12, 15, 17, 23, and 25 in a genome-wide association study with single nucleotide polymorphisms. Further, fine-mapping of target regions on BTA 2 and 3, identified several associated variants and potential candidate genes underlying endocrine fertility traits. Subsequently, the optimal use of endocrine fertility traits in genomic evaluations was investigated; using empirical and theoretical predictions for single-trait models, I showed that endocrine fertility traits have more predictive ability than classical fertility traits. The accuracy of genomic prediction was also substantially improved when endocrine and classical fertility traits were combined in multi-trait genomic prediction. Finally, using deterministic predictions, the potential accuracy of multi-trait genomic selection when combining a cow training population measured for the endocrine trait commencement of luteal activity (C-LA), with a training population of bulls with daughter observations for a classical fertility trait was investigated. Results showed that for prediction of fertility, there is no benefit of investing in a cow training population when the breeding goal is based on classical fertility traits. However, when considering a more biological breeding goal for fertility like C-LA, accuracy is substantially improved when endocrine traits are available from a limited number of farms

Genetics and genomics of fertility in dairy cows

The existence of significant and sufficient genetic variation in fertility is generally accepted and most leading dairy cattle breeding programmes have included fertility in their selection indices (Miglior and others 2005). This multi-trait selection has been very effective and reversed the negative genetic trend for fertility. Selection is based on traits derived from calving dates and insemination dates. These traits are biased by farm management decisions, whereas endocrine fertility phenotypes reflect a cow's physiology directly and are thus better than fertility traits for animal breeding. Genomic selection (Meuwissen and others 2001) predicts breeding values for a large number of genetic markers across the entire genome. Genomic selection will improve the rate of progress for the fertility traits. Current developments include the use of whole genome sequence information, with the prospect of using the causal mutations for selection, and the use of in-line progesterone measures to develop better fertility traits. For the future this holds the promise that farmers should spend less money, treatments and labour to achieve optimal fertility for their herd, since the genetic potential for fertility will improve due to more effective selection

Magnetic Technologies and Green Solvents in Extraction and Separation of Bioactive Molecules Together with Biochemical Objects: Current Opportunities and Challenges

Currently, magnetic technology and green solvents are widely used in chemical engineering, environmental engineering and other fields as they are environmentally friendly, easy to operate and highly efficient. Moreover, a magnetic field has positive effect on many physicochemical processes. However, related new methods, materials, strategies and applications in separation science still need to be developed. In this review, a series of meaningful explorations of magnetic technologies for the separation of natural products and biologic objects, including magnetic ionic liquids and other magnetic solvents and fluids, magnetic nanoparticles and magnetic fields, and the development of magnetic separators were reviewed. Furthermore, the difficulties in the application and development of magnetic separation technology were discussed on the basis of comparison and data analysis, especially for the selection of magnetic materials and magnetic field sources. Finally, the progress in the development of magnetic separators was also elaborated for researchers, mainly including that of the new high-efficiency magnetic separator through multi-technology integration and the optimization of traditional magnetic separators, which help current techniques break through their bottleneck as a powerful driving force

A QoS Evolutionary Method of Cloud Service Based on User Utility Model

The quality-of-service (QoS) is a common focus which users and service providers pay close attention to at present. For service providers, it is one of their main targets to find the optimal QoS strategy based on user preferences. However, because of the fuzziness of user preferences and the complexity of service environment, searching an optimal service strategy becomes a difficult problem. In the paper, how the QoS affects a user\u27s satisfaction is analyzed, and then a quantitative relationship between QoS and user satisfaction is built. Based on the relationship, a user utility model of cloud service is established. In order to maximize user utility, a QoS evolutionary algorithm based on user utility model is proposed. In the algorithm, some improvement is designed to balance the contradiction between search scope and search speed in the traditional genetic algorithm. It can be seen through the experiments that the QoS optimization strategy of cloud service output by the QoS evolutionary algorithm is consistent with the target user\u27s preferences, which can effectively enhance the cost effectiveness of service resources

Heritability of shape in common sole, Solea solea, estimated from image analysis data

The spider crab Maja squinado is an endangered Mediterranean species; therefore, culturing it successfully is essential for developing restocking programs. The survival, growth and development of post-larval stages (juvenile crabs, C1–C8) were studied using larvae obtained from adult individuals collected in the Catalan Sea. The juvenile crab stages were cultured individually from a megalopal stage using a semi-open recirculation system to obtain the precise growth data of each juvenile crab stage until C8. Development up to C8 at 20 °C lasted 154 ± 10 days. Survival from C1 to C8 was 5.8%. Moult increment values in cephothoracic length were similar in all the crab stages (21–35%). Intermoult duration (9 ± 1 in C1–C2 to 51 ± 8 days in C7–C8) increased sharply from juvenile stage 5. Males and females can be distinguished from C4 based on sexual dimorphism in the pleopods and the presence of gonopores. The allometric growth of the pleon is sex-dependent from C4, with females showing positive allometry and males isometric growth. The juvenile growth rate was lower compared with that of the previously studied Atlantic species Maja brachydactyla

Improving accuracy of bulls' predicted genomic breeding values for fertility using daughters' milk progesterone profiles

The main objective of this study was to investigate the benefit of accuracy of genomic prediction when combining records for an intermediate physiological phenotype in a training population with records for a traditional phenotype. Fertility was used as a case study, where commencement of luteal activity (C-LA) was the physiological phenotype, whereas the interval from calving to first service and calving interval were the traditional phenotypes. The potential accuracy of across-country genomic prediction and optimal recording strategies of C-LA were also investigated in terms of the number of farms and number of repeated records for C-LA. Predicted accuracy was obtained by estimating population parameters for the traits in a data set of 3,136 Holstein Friesian cows with 8,080 lactations and using a deterministic prediction equation. The effect of genetic correlation, heritability, and reliability of C-LA on the accuracy of genomic prediction were investigated. When the existing training population was 10,000 bulls with reliable estimated breeding value for the traditional trait, predicted accuracy for the physiological trait increased from 0.22 to 0.57 when 15,000 cows with C-LA records were added to the bull training population; but, when the interest was in predicting the traditional trait, we found no benefit from the additional recording. When the genetic correlation was higher between the physiological and traditional traits (0.7 instead of 0.3), accuracy increased less when adding the 15.000 cows with C-LA (from 0.51 to 0.63). In across-country predictions, we observed little to no increase in accuracy of the intermediate physiological phenotype when the training population from Sweden was large, but when accuracy increased the training population was small (200 cows), from 0.19 to 0.31 when 15,000 cows were added from the Netherlands (genetic correlation of 0.5 between countries), and from 0.19 to 0.48 for genetic correlation of 0.9. The predicted accuracy initially increased substantially when recording on the same farm was extended and multiple C-LA records per cow were used in prediction compared with single records; that is, accuracy increased from 0.33 with single records to 0.38 with multiple records (on average 1.6 records per cow) from 2 yr of recording C-LA. But, when the number C-LA per cow increased beyond 2 yr of recording, we noted no substantial benefit in accuracy from multiple records. For example, for 5 yr of recording (on average 2.5 records per cow), accuracy was 0.47; on doubling the recording period to 10 yr (on average 3.1 records per cow), accuracy increased by 0.07 units, whereas when C-LA was recorded for 15 yr (on average 3.3 records per cow) accuracy increased only by 0.05 units. Therefore, for genomic prediction using expensive equipment to record traits for training populations, it is important to optimize the recording strategy. The focus should be on recording more cows rather than continuous recording on the same cows

Opportunities for genomic prediction for fertility using endocrine and classical fertility traits in dairy cattle

Endocrine fertility traits, defined from progesterone concentration levels in milk, have been suggested as alternative indicators for fertility in dairy cows because they are less biased by farm management decisions and more directly reflect a cow’s reproductive physiology than classical traits derived from insemination and calving data. To determine the potential use of endocrine fertility traits in genomic evaluations, the improvement in accuracy from using endocrine fertility traits concurrent with classical traits in the genomic prediction of fertility was quantified. The impact of recording all traits on all training animals was also investigated. Endocrine and classical fertility records were available on 5,339 lactations from 2,447 Holstein cows in Ireland, the Netherlands, Sweden, and the United Kingdom. The endocrine traits were commencement of luteal activity (C-LA]) and proportion of samples with luteal activity (PLA); the classical trait was the interval from calving to first service (CFS). The interval from C-LA to first service (C-LAFS), which is a combination of an endocrine trait and a classical trait, was also investigated. The target (breeding goal) trait for fertility was CFS or C-LAFS, whereas C-LA and PLA served as predictor traits. Genomic EBV (GEBV) for fertility were derived using genomic BLUP in bivariate models with 85,485 SNP. Genomic EBV for the separate fertility traits were also computed, in univariate models. The accuracy of GEBV was evaluated by 5-fold cross-validation. The highest accuracy of GEBV was achieved using bivariate predictions, where both an endocrine fertility trait and the classical fertility trait were used. Accuracy of GEBV for predicting adjusted phenotypes for CFS in the univariate model was 0.04, but when predicting CFS using a bivariate model with C-LA, the accuracy increased to 0.14 when all training animals were phenotyped for C-LA and (or not) for CFS. On phenotyping all training animals for both C-LA and CFS, accuracy for CFS increased to 0.18; however, when validation animals were also phenotyped for C-LA, there was no substantial increase in accuracy. When predicting CFS in bivariate analysis with PLA, accuracy ranged from 0.07 to 0.14. This first study on genomic predictions for fertility using endocrine traits suggests some improvement in the accuracy of prediction over using only the classical traits. Further studies with larger training populations may show greater improvements.</p