Comparative oil extraction from mutt (Myliobatis goodei) liver by enzymatic hydrolysis: free versus immobilized biocatalyst.

BACKGROUND: The development and fine-tuning of biotechnological processes for fish oil extraction constitute a very important focus to contribute to the development of a food industry based on fish consumption. This work lies in a comparative analysis of the oil extraction yield of Myliobatis goodei livers using free and immobilized enzymes. RESULTS: An immobilized biocatalyst was designed from the cell-free extract of a Bacillus sp. Mcn4. A complete factorial design was used to study the components of the bacterial culture medium and select the condition with the highest titers of extracellular enzymatic activities. Wheat bran had a significant effect on the culture medium composition for enzymatic production. The immobilized biocatalyst was designed by covalent binding of the proteins present in the cocktail retaining a percentage of different types of enzymatic activities (Mult.Enz@MgFe2 O4 ). Among the biocatalyst used, Alcalase® 2.4 L and Purazyme® AS 60 L (free commercial proteases) showed extraction yields of 87.39% and 84.25%, respectively, while Mult.Enz@MgFe2 O4 achieved a better one of 89.97%. The oils obtained did not show significant differences in their physical-chemical properties while regarding the fatty acid content, the oil extracted with Purazyme® AS 60 L showed a comparatively lower proportion of polyunsaturated fatty acids. CONCLUSIONS: Our results suggest that the use of by-products of M. goodei is a valid alternative and encourages the use of immobilized multienzyme biocatalysts for the treatment of complex substrates in the fishing industry. © 2023 Society of Chemical Industry.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling.

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

Interfacial Hyperactivation of Candida rugosa Lipase onto Ca2Fe2O5 Nanoparticles: pH and Ionic Strength Fine-Tuning to Modulate Protein-Support Interactions.

The current study provides a comprehensive look of the adsorption process of Candida rugosa lipase (CRL) on Ca2Fe2O5 iron oxide nanoparticles (NPs). Protein-support interactions were identified across a broad range of pH and ionic strengths (mM) through a response surface methodology, surface charge determination, and spectroscopic and in silico analyses. The maximum quantity of immobilized protein was achieved at an ionic strength of 50 mM and pH 4. However, this condition did not allow for the greatest hydrolytic activity to be obtained. Indeed, it was recorded at acidic pH, but at 150 mM, where evaluation of the recovered activity revealed hyperactivation of the enzyme. These findings were supported by adsorption isotherms performed under different conditions. Based on zeta potential measurements, electrostatic interactions contributed differently to protein-support binding under the conditions tested, showing a strong correlation with experimentally determined immobilization parameters. Raman spectra revealed an increase in hydrophobicity around tryptophan residues, whereas the enzyme immobilization significantly reduced the phenylalanine signal in CRL. This suggests that this residue was involved in the interaction with Ca2Fe2O2 and molecular docking analysis confirmed these findings. Fluorescence spectroscopy showed distinct behaviors in the CRL emission patterns with the addition of Ca2Fe2O5 at pH 4 and 7. The calculated thermodynamic parameters indicated that the contact would be mediated by hydrophobic interactions at both pHs, as well as by ionic ones at pH 4. In this approach, this work adds to our understanding of the design of biocatalysts immobilized in iron oxide NPs.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery.

Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a "bottom-up" approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

Hybridization and polyploidization effects on LTR-retrotransposon activation in potato genome.

Hybridization and polyploidization are major forces in plant evolution and potatoes are not an exception. It is proposed that the proliferation of Long Terminal Repeat-retrotransposons (LTR-RT) is related to genome reorganization caused by hybridization and/or polyploidization. The main purpose of the present work was to evaluate the effect of interspecific hybridization and polyploidization on the activation of LTR-RT. We evaluated the proliferation of putative active LTR-RT in a diploid hybrid between the cultivated potato Solanum tuberosum and the wild diploid potato species S. kurtzianum, allotetraploid lines derived from this interspecific hybrid and S. kurtzianum autotetraploid lines (ktz-autotetraploid) using the S-SAP (sequence-specific amplified polymorphism) technique and normalized copy number determination by qPCR. Twenty-nine LTR-RT copies were activated in the hybrid and present in the allotetraploid lines. Major LTR-RT activity was detected in Copia-27, Copia-12, Copia-14 and, Gypsy-22. According to our results, LTR-RT copies were activated principally in the hybrid, there was no activation in allotetraploid lines and only one copy was activated in the autotetraploid.

Molecular Events behind the Selectivity and Inactivation Properties of Model NaK-Derived Ion Channels.

Y55W mutants of non-selective NaK and partly K+-selective NaK2K channels have been used to explore the conformational dynamics at the pore region of these channels as they interact with either Na+ or K+. A major conclusion is that these channels exhibit a remarkable pore conformational flexibility. Homo-FRET measurements reveal a large change in W55-W55 intersubunit distances, enabling the selectivity filter (SF) to admit different species, thus, favoring poor or no selectivity. Depending on the cation, these channels exhibit wide-open conformations of the SF in Na+, or tight induced-fit conformations in K+, most favored in the four binding sites containing NaK2K channels. Such conformational flexibility seems to arise from an altered pattern of restricting interactions between the SF and the protein scaffold behind it. Additionally, binding experiments provide clues to explain such poor selectivity. Compared to the K+-selective KcsA channel, these channels lack a high affinity K+ binding component and do not collapse in Na+. Thus, they cannot properly select K+ over competing cations, nor reject Na+ by collapsing, as K+-selective channels do. Finally, these channels do not show C-type inactivation, likely because their submillimolar K+ binding affinities prevent an efficient K+ loss from their SF, thus favoring permanently open channel states.

Peimine, an Anti-Inflammatory Compound from Chinese Herbal Extracts, Modulates Muscle-Type Nicotinic Receptors.

Fritillaria bulbs are used in Traditional Chinese Medicine to treat several illnesses. Peimine (Pm), an anti-inflammatory compound from Fritillaria, is known to inhibit some voltage-dependent ion channels and muscarinic receptors, but its interaction with ligand-gated ion channels remains unexplored. We have studied if Pm affects nicotinic acetylcholine receptors (nAChRs), since they play broad functional roles, both in the nervous system and non-neuronal tissues. Muscle-type nAChRs were incorporated to Xenopus oocytes and the action of Pm on the membrane currents elicited by ACh (IAChs) was assessed. Functional studies were combined with virtual docking and molecular dynamics assays. Co-application of ACh and Pm reversibly blocked IACh, with an IC50 in the low micromolar range. Pm inhibited nAChR by: (i) open-channel blockade, evidenced by the voltage-dependent inhibition of IAch, (ii) enhancement of nAChR desensitization, revealed by both an accelerated IACh decay and a decelerated IACh deactivation, and (iii) resting-nAChR blockade, deduced from the IACh inhibition elicited by Pm when applied before ACh superfusion. In good concordance, virtual docking and molecular dynamics assays demonstrated that Pm binds to different sites at the nAChR, mostly at the transmembrane domain. Thus, Pm from Fritillaria bulbs, considered therapeutic herbs, targets nAChRs with high affinity, which might account for its anti-inflammatory actions.

Modulation of Function, Structure and Clustering of K+ Channels by Lipids: Lessons Learnt from KcsA.

KcsA, a prokaryote tetrameric potassium channel, was the first ion channel ever to be structurally solved at high resolution. This, along with the ease of its expression and purification, made KcsA an experimental system of choice to study structure-function relationships in ion channels. In fact, much of our current understanding on how the different channel families operate arises from earlier KcsA information. Being an integral membrane protein, KcsA is also an excellent model to study how lipid-protein and protein-protein interactions within membranes, modulate its activity and structure. In regard to the later, a variety of equilibrium and non-equilibrium methods have been used in a truly multidisciplinary effort to study the effects of lipids on the KcsA channel. Remarkably, both experimental and "in silico" data point to the relevance of specific lipid binding to two key arginine residues. These residues are at non-annular lipid binding sites on the protein and act as a common element to trigger many of the lipid effects on this channel. Thus, processes as different as the inactivation of channel currents or the assembly of clusters from individual KcsA channels, depend upon such lipid binding.

Accessibility of Cations to the Selectivity Filter of KcsA in the Inactivated State: An Equilibrium Binding Study.

Cation binding under equilibrium conditions has been used as a tool to explore the accessibility of permeant and nonpermeant cations to the selectivity filter in three different inactivated models of the potassium channel KcsA. The results show that the stack of ion binding sites (S1 to S4) in the inactivated filter models remain accessible to cations as they are in the resting channel state. The inactivated state of the selectivity filter is therefore "resting-like" under such equilibrium conditions. Nonetheless, quantitative differences in the apparent KD's of the binding processes reveal that the affinity for the binding of permeant cations to the inactivated channel models, mainly K⁺, decreases considerably with respect to the resting channel. This is likely to cause a loss of K⁺ from the inactivated filter and consequently, to promote nonconductive conformations. The most affected site by the affinity loss seems to be S4, which is interesting because S4 is the first site to accommodate K⁺ coming from the channel vestibule when K⁺ exits the cell. Moreover, binding of the nonpermeant species, Na⁺, is not substantially affected by inactivation, meaning that the inactivated channels are also less selective for permeant versus nonpermeant cations under equilibrium conditions.

Selective exclusion and selective binding both contribute to ion selectivity in KcsA, a model potassium channel.

The selectivity filter in potassium channels, a main component of the ion permeation pathway, configures a stack of binding sites (sites S1-S4) to which K+ and other cations may bind. Specific ion binding to such sites induces changes in the filter conformation, which play a key role in defining both selectivity and permeation. Here, using the potassium channel KcsA as a model, we contribute new evidence to reinforce this assertion. First, ion binding to KcsA blocked by tetrabutylammonium at the most cytoplasmic site in the selectivity filter (S4) suggests that such a site, when in the nonconductive filter conformation, has a higher affinity for cation binding than the most extracellular S1 site. This filter asymmetry, along with differences in intracellular and extracellular concentrations of K+versus Na+ under physiological conditions, should strengthen selection of the permeant K+ by the channel. Second, we used different K+ concentrations to shift the equilibrium between nonconductive and conductive states of the selectivity filter in which to test competitive binding of Na+ These experiments disclosed a marked decrease in the affinity of Na+ to bind the channel when the conformational equilibrium shifts toward the conductive state. This finding suggested that in addition to the selective binding of K+ and other permeant species over Na+, there is a selective exclusion of nonpermeant species from binding the channel filter, once it reaches a fully conductive conformation. We conclude that selective binding and selective exclusion of permeant and nonpermeant cations, respectively, are important determinants of ion channel selectivity.

Favipiravir and Ribavirin Treatment of Epidemiologically Linked Cases of Lassa Fever.

Two patients with Lassa fever are described who are the first human cases treated with a combination of ribavirin and favipiravir. Both patients survived but developed transaminitis and had prolonged detectable virus RNA in blood and semen, suggesting that the possibility of sexual transmission of Lassa virus should be considered.

Towards understanding the molecular basis of ion channel modulation by lipids: Mechanistic models and current paradigms.

Research on ion channel modulation has become a hot topic because of the key roles these membrane proteins play in both prokaryotic and eukaryotic organisms. In this respect, lipid modulation adds to the overall modulatory mechanisms as a potential via to find new pharmacological targets for drug design based on interfering with lipid/channel interactions. However, our knowledge in this field is scarce and often circumscribed to the sites where lipids bind and/or its final functional consequences. To fully understand this process it is necessary to improve our knowledge on its molecular basis, from the binding sites to the signalling pathways that derive in structural and functional effects on the ion channel. In this review, we have compiled information about such mechanisms and established a classification into four different modes of action. Afterwards, we have revised in more detail the lipid modulation of Cys-loop receptors and of the potassium channel KcsA, which were chosen as model channels modulated by specific lipids. This article is part of a Special Issue entitled: Membrane Lipid Therapy: Drugs Targeting Biomembranes edited by Pablo V. Escribá.

Differential binding of monovalent cations to KcsA: Deciphering the mechanisms of potassium channel selectivity.

This work explores whether the ion selectivity and permeation properties of a model potassium channel, KcsA, could be explained based on ion binding features. Non-permeant Na+ or Li+ bind with low affinity (millimolar KD's) to a single set of sites contributed by the S1 and S4 sites seen at the selectivity filter in the KcsA crystal structure. Conversely, permeant K+, Rb+, Tl+ and even Cs+ bind to two different sets of sites as their concentration increases, consistent with crystallographic evidence on the ability of permeant species to induce concentration-dependent transitions between conformational states (non-conductive and conductive) of the channel's selectivity filter. The first set of such sites, assigned also to the crystallographic S1 and S4 sites, shows similarly high affinities for all permeant species (micromolar KD's), thus, securing displacement of potentially competing non-permeant cations. The second set of sites, available only to permeant cations upon the transition to the conductive filter conformation, shows low affinity (millimolar KD's), thus, favoring cation dissociation and permeation and results from the contribution of all S1 through S4 crystallographic sites. The differences in affinities between permeant and non-permeant cations and the similarities in binding behavior within each of these two groups, correlate fully with their permeabilities relative to K+, suggesting that binding is an important determinant of the channel's ion selectivity. Conversely, the complexity observed in permeation features cannot be explained just in terms of binding and likely relates to reported differences in the occupancy of the S2 and S3 sites by the permeant cations.

Competing Lipid-Protein and Protein-Protein Interactions Determine Clustering and Gating Patterns in the Potassium Channel from Streptomyces lividans (KcsA).

There is increasing evidence to support the notion that membrane proteins, instead of being isolated components floating in a fluid lipid environment, can be assembled into supramolecular complexes that take part in a variety of cooperative cellular functions. The interplay between lipid-protein and protein-protein interactions is expected to be a determinant factor in the assembly and dynamics of such membrane complexes. Here we report on a role of anionic phospholipids in determining the extent of clustering of KcsA, a model potassium channel. Assembly/disassembly of channel clusters occurs, at least partly, as a consequence of competing lipid-protein and protein-protein interactions at nonannular lipid binding sites on the channel surface and brings about profound changes in the gating properties of the channel. Our results suggest that these latter effects of anionic lipids are mediated via the Trp(67)-Glu(71)-Asp(80) inactivation triad within the channel structure and its bearing on the selectivity filter.

Study of the Interactions of Fusarium virguliforme Toxin FvTox1 with Synthetic Peptides by Molecular Simulations and a Label-Free Biosensor.

Fusarium virguliforme is a soil borne pathogen that causes sudden death syndrome (SDS) in soybean plants. This pathogenic disease may result in severe soybean yield suppression and can cause serious economic harm. It has been shown that the FvTox1 toxin produced by the pathogen may be the root cause of foliar SDS. Anti-FvTox1 single-chain variable fragment antibody expressed in transgenic soybean plants was shown to neutralize the FvTox1 toxin involved in foliar SDS development. Here, we have investigated the binding affinities of FvTox1 with four FvTox1-interacting peptides of 7 to 12 amino acids identified from phage display libraries using both bioinformatics-based molecular simulations and label-free bioassays with a unique photonic crystal biosensor. Results from the molecular simulations have predicted the interaction energies and 3-dimensional (3D) structures of FvTox1 and FvTox1-interacting peptide complexes. Our label-free binding assays have further provided the interaction strength of FvTox1 with four different FvTox1-interacting peptides and experimentally confirmed the simulation results obtained from bioinformatics-based molecular calculations.

Use of 3D Prototypes for Complex Surgical Oncologic Cases.

INTRODUCTION: Physical 3D models known by the industry as rapid prototyping involve the creation of a physical model from a 3D computer version. In recent years, there has been an increasing number of reports on the use of 3D models in medicine. Printing such 3D models with different materials integrating the many components of human anatomy is technically challenging. In this article, we report our technological developments along with our clinical implementation experience using high-fidelity 3D prototypes of tumors encasing major vessels in anatomically sensitive areas. METHODS: Three patients with tumors encasing major vessels that implied complex surgery were selected for surgical planning using 3D prototypes. 3D virtual models were obtained from routine CT and MRI images. The models, with all their anatomical relations, were created by an expert pediatric radiologist and a surgeon, image by image, along with a computerized-aided design engineer. RESULTS: Surgeons had the opportunity to practice on the model before the surgery. This allowed questions regarding surgical approach; feasibility and potential complications to be raised in advance of the actual procedure. All patients then successfully underwent surgery as planned. CONCLUSION: Having a tumor physically printed in its different main component parts with its anatomical relationships is technically feasible. Since a gross total resection is prognostic in a significant percentage of tumor types, refinements in planning may help achieve greater and safer resections therefore contributing to improve surgical management of complex tumors. In this early experience, 3D prototyping helped significantly in the many aspects of surgical oncology planning.

Frequency and tendency of malaria in Colombia, 1990 to 2011: a descriptive study.

BACKGROUND: Malaria is a serious health problem in Colombia. This paper intends to analyse the frequency and tendencies of the disease in Colombia over the last 22 years. The researchers used the Box-Jenkins (ARIMA) methodology for the analysis of time series. METHODS: This descriptive study was done retrospectively by using the morbidity records of the Ministry of Health and of the System for the Monitoring of Public Health (SIVIGILA). The information about the population was obtained from the National Administrative Department of Statistics (DANE). The incidence rate according to age and sex was calculated from 1990 to 2011. Also, the Annual Parasite Index (API) for Plasmodium falciparum and for Plasmodium vivax was calculated. The mortality rates per year, from 1990 to 2011, were determined. Finally, the Box-Jenkins (ARIMA) methodology was used for the analysis of time series, grouped weekly. Information for ARIMA modelling was used from the year 2001. RESULTS: The total number of reported cases from 1990 to 2011 was 2,964,818 cases with an annual average of 134,764. In the period from 2001 to 2005 and from 2006 to 2011 a significant decrease of annual cases was observed. In general, a predominance of P. vivax over P. falciparum was observed. With respect to the API, it must be noted that there were peaks in 1994 in the departments of Guainía and Guaviare, and in 1998 in Guaviare and Chocó. The department of Antioquia showed a tendency towards a decrease of the API through the years.In the time series model there were no statistically significant seasonal patterns for the total number of cases of malaria. However, for P. falciparum the number of cases was statistically significant. Lastly, between 1990 and 2009, there were 1,905 deaths caused by malaria in Colombia with a significant tendency towards a decrease in deaths over those years. Plasmodium falciparum was more lethal than P. vivax. CONCLUSIONS: In Colombia, the transmission of malaria occurs in an endemic and epidemic context, which keeps an unstable endemic transmission pattern. Several factors specific to a country such as Colombia encourage the dissemination and permanence of the illness.

The use of ROC analysis for the qualitative prediction of human oral bioavailability from animal data.

PURPOSE: To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (Fhuman) from animal oral bioavailability (Fanimal) data employing ROC analysis and to identify the optimal thresholds for such predictions. METHODS: A dataset of 184 compounds with known Fhuman and Fanimal in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for Fhuman was built by setting a threshold for high/low Fhuman at 50%. The thresholds for high/low Fanimal were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from 'cost analysis' and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions. RESULTS: We successfully built ROC curves for the combined dataset and per individual species. Optimal Fanimal thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS. CONCLUSIONS: Fanimal can predict high/low Fhuman with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection.

Microbiological Diversity and Associated Enzymatic Activities in Honey and Pollen from Stingless Bees from Northern Argentina.

Honey and pollen from Tetragonisca fiebrigi and Scaptotrigona jujuyensis, stingless bees from northern Argentina, presented a particular microbiological profile and associated enzymatic activities. The cultured bacteria were mostly Bacillus spp. (44%) and Escherichia spp. (31%). The phylogenetic analysis showed a taxonomic distribution according to the type of bee that was similar in both species. Microbial enzymatic activities were studied using hierarchical clustering. Bacillus spp. was the main bacterium responsible for enzyme production. Isolates with xylanolytic activity mostly presented cellulolytic activity and, in fewer cases, lipolytic activity. Amylolytic activity was associated with proteolytic activity. None of the isolated strains produced multiple hydrolytic enzymes in substantial amounts, and bacteria were classified according to their primary hydrolytic activity. These findings add to the limited knowledge of microbiological diversity in honey and pollen from stingless bees and also provide a physiological perspective of this community to assess its biotechnological potential in the food industry.