Female Voice-Related Sexual Attractiveness to Males: Does it Vary With Different Degrees of Conception Likelihood?

Previous investigations have found that female voice-related attractiveness to males increases when both conception likelihood (CL) and voice fundamental frequency (fo) are elevated. To test this hypothesis, we conducted a perceptual experiment where 78 heterosexual males rated sexual attractiveness of 9 female voice samples, recorded at menstrual, follicular and luteal phases of the menstrual cycle under two double-blinded randomly allocated conditions: a natural menstrual cycle (placebo condition) and when using an oral contraceptive pill (OCP condition). The voice samples yielded a total of 54 stimuli that were visually sorted and rated using Visor software. Concentrations of estrogens, progesterone and testosterone were analyzed, and measurements of speaking fundamental frequency (sfo) and its standard deviation (sfoSD), fo derivative (dfo) and fo slope were made. A multilevel ordinal logistic regression model nested in listeners and in females, and adjusted by phase and condition, was carried out to assess the association between ratings and: (1) phases and conditions; (2) sex steroid hormonal concentrations; and (3) voice parameters. A high probability of obtaining high ratings of voice sexual attractiveness was found for: (1) menstrual phase of placebo use and follicular phase of OCP use; (2) for low estradiol to progesterone ratio and testosterone concentrations; and (3) for low dfo. The latter showed a moderate statistical association with ratings of high attractiveness, as compared with the small association found for the remaining variables. It seems that the voice is a weak cue for female CL. Female sexual attraction to males may be a consequence of what females do in order to regulate their extended sexuality across the menstrual cycle rather than of estrus cues, the use of paralinguistic speech patterns being an example.

A Stepwise Framework for the Systematic Development of Lipid Nanoparticles.

A properly designed nanosystem aims to deliver an optimized concentration of the active pharmaceutical ingredient (API) at the site of action, resulting in a therapeutic response with reduced adverse effects. Due to the vast availability of lipids and surfactants, producing stable lipid dispersions is a double-edged sword: on the one hand, the versatility of composition allows for a refined design and tuning of properties; on the other hand, the complexity of the materials and their physical interactions often result in laborious and time-consuming pre-formulation studies. However, how can they be tailored, and which premises are required for a "right at first time" development? Here, a stepwise framework encompassing the sequential stages of nanoparticle production for disulfiram delivery is presented. Drug in lipid solubility analysis leads to the selection of the most suitable liquid lipids. As for the solid lipid, drug partitioning studies point out the lipids with increased capacity for solubilizing and entrapping disulfiram. The microscopical evaluation of the physical compatibility between liquid and solid lipids further indicates the most promising core compositions. The impact of the outer surfactant layer on the colloidal properties of the nanosystems is evaluated recurring to machine learning algorithms, in particular, hierarchical clustering, principal component analysis, and partial least squares regression. Overall, this work represents a comprehensive systematic approach to nanoparticle formulation studies that serves as a basis for selecting the most suitable excipients that comprise solid lipid nanoparticles and nanostructured lipid carriers.

Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.

Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.

Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms.

Cationic compounds have been described to readily penetrate cell membranes. Assigning positive charge to nanosystems, e.g. lipid nanoparticles, has been identified as a key feature to promote electrostatic binding and design ligand-based constructs for tumour targeting. However, their intrinsic high cytotoxicity has hampered their biomedical application. This paper seeks to establish which cationic compounds and properties are compelling for interface modulation, in order to improve the design of tumour targeted nanoparticles against glioblastoma. How can intrinsic features (e.g. nature, structure, conformation) shape efficacy outcomes? In the quest for safer alternative cationic compounds, we evaluate the effects of two novel glycerol-based lipids, GLY1 and GLY2, on the architecture and performance of nanostructured lipid carriers (NLCs). These two molecules, composed of two alkylated chains and a glycerol backbone, differ only in their polar head and proved to be efficient in reversing the zeta potential of the nanosystems to positive values. The use of unsupervised and supervised machine learning (ML) techniques unraveled their structural similarities: in spite of their common backbone, GLY1 exhibited a better performance in increasing zeta potential and cytotoxicity, while decreasing particle size. Furthermore, NLCs containing GLY1 showed a favorable hemocompatible profile, as well as an improved uptake by tumour cells. Summing-up, GLY1 circumvents the intrinsic cytotoxicity of a common surfactant, CTAB, is effective at increasing glioblastoma uptake, and exhibits encouraging anticancer activity. Moreover, the use of ML is strongly incited for formulation design and optimization.

Diving into Batch-to-Batch Variability of Topical Products-a Regulatory Bottleneck.

PURPOSE: Following the recent European Medicine Agency (EMA) draft guideline on quality and equivalence of topical products, a modular framework for bioequivalence assessment is proposed, wherein the qualitative, quantitative, microstructure and product performance sameness is demanded to support generic applications. Strict regulatory limits are now imposed, but, the suitability of these limits has been subject of intense debate. In this context, this paper aims to address these issues by characterizing a panel of 8 reference blockbuster semisolid topical products. METHODS: For each product, three batches were selected and, whenever possible, batches retrieved from different manufacturing sites were considered. Product microstructure was evaluated in terms of globule size, pH, rheological attributes and, if required, the thermal behaviour was also assessed. Performance was evaluated through in vitro release testing (IVRT). Finally, an integrated multivariate analysis was performed to highlight the features that most contribute for product variability. RESULTS: Marked differences were registered within reference products. Statistical analysis demonstrated that if EMA criteria are applied, none of the same product batches can be considered as equivalent. Rheological parameters as well as IVRT indicators account for the majority of batch-to-batch differences. CONCLUSIONS: Semisolid dosage forms exhibit intrinsic variability. This calls for the attention to the need of establishing reasonable equivalence criteria applied to generic drug products. Graphical abstract.

Peptide-lipid nanoconstructs act site-specifically towards glioblastoma growth impairment.

Ultra-small nanostructured lipid carriers (usNLCs) have been hypothesized to promote site-specific glioblastoma (GB) drug delivery. Envisioning a multitarget purpose towards tumor cells and microenvironment, a surface-bioconjugated usNLC prototype is herein presented. The comeback of co-delivery by repurposing atorvastatin and curcumin, as complementary therapy, was unveiled and characterized, considering colloidal properties, stability, and drug release behavior. Specifically, the impact of the surface modification of usNLCs with hyaluronic acid (HA) conjugates bearing the cRGDfK and H7k(R2)2 peptides, and folic acid (FA) on GB cells was sequentially evaluated, in terms of cytotoxicity, internalization, uptake mechanism and hemolytic character. As proof-of-principle, the biodistribution, tolerability, and efficacy of the nanocarriers were assessed, the latter in GB-bearing mice through magnetic resonance imaging and spectroscopy. The hierarchical modification of the usNLCs promotes a preferential targeting behavior to the brain, while simultaneously sparing the elimination by clearance organs. Moreover, usNLCs were found to be well tolerated by mice and able to impair tumor growth in an orthotopic xenograft model, whereas for mice administered with the non-encapsulated therapeutic compounds, tumor growth exceeded 181% in the same period. Relevant biomarkers extracted from metabolic spectroscopy were ultimately identified as a potential tumor signature.

Biomimeting ultra-small lipid nanoconstructs for glioblastoma treatment: A computationally guided experimental approach.

Ultra-small nanostructured lipid carriers (usNLCs) are stable, biocompatible and biodegradable colloidal systems, claiming a broad set of advanced features suitable for cancer drug delivery. To unleash their potential in glioblastoma research and therapy, we have developed an usNLC prototype able to co-encapsulate atorvastatin calcium and curcumin, as repurposed drugs previously screened from molecular dynamics simulations. The novelty not only relies on the drug repositioning approach, but also on a robust computational methodology utilized for formulation optimization, under the umbrella of multivariate analysis and full factorial designs. A coating procedure with red blood cell membranes is ultimately hypothesized, aiming at integrating the biomimetic concept into usNLCs for glioblastoma therapeutics. The formulation composition and process parameters, that demonstrated a high-risk level for the final quality and performance of the usNLCs, include the solid:liquid lipid ratio, type and concentration of liquid lipids and surfactants, along with the type of production method. Particles with an average diameter of ca. 50 nm, and a polydispersity index lower than 0.3 were produced, exhibiting high stability, up-scalability, drug protection and sustained co-release properties, meeting the suitable critical quality attributes for intravenous administration. Also, a Taguchi design was successfully applied to optimizing usNLCs as cell membrane-coating technology.

Aptamer-peptide conjugates as a new strategy to modulate human α-thrombin binding affinity.

Aptamers are single-stranded RNA or DNA molecules that specifically recognize their targets and have proven valuable for functionalizing sensitive biosensors. α-thrombin is a trypsin-like serine proteinase which plays a crucial role in haemostasis and thrombosis. An abnormal activity or overexpression of this protein is associated with a variety of diseases. A great deal of attention was devoted to the construction of high-throughput biosensors for accurately detect thrombin for the early diagnosis and treatment of related diseases. Herein, we propose a new approach to modulate the interaction between α-thrombin and the aptamer TBA15. To this end, TBA15 was chemically conjugated to two peptide sequences (TBA-G3FIE-Ac and TBA-G3EIF-Ac) corresponding to a short fragment of the acidic region of the human factor V, which is known to interact directly with exosite I. Surface Plasmon Resonance (SPR) results showed enhanced analytical performances of thrombin with TBA-G3EIF-Ac than with TBA wild-type, reaching a limit of detection as low as 44.9 pM. Electrophoresis mobility shift assay (EMSA) corroborated the SPR results. Molecular dynamics (MD) simulations support experimental evidences and provided further insight into thrombin/TBA-peptide interaction. Our findings demonstrate that the combination of TBA15 with key interacting peptides offers good opportunities to produce sensitive devices for thrombin detection and potential candidates to block thrombin activity.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data.

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Host flexibility and space filling in supramolecular complexation of cyclodextrins: A free-energy-oriented approach.

Cyclodextrins (Cds) are versatile carbohydrate hosts for developing multifunctional nanostructures of pharmaceutical interest. Factors affecting the thermodynamic signatures and stability of ß- and γ-Cd complexes are detailed at the atomic level. The MD/PMF-based method is combined with the description of the nature and strength of the inter-partner affinity. Naphthalene, adamantane and lycorine derivatives are used as models of drug-leading structures. Guest size affects Cd-guest contact and the inclusion degree, inducing Cd deformation, which opposes inclusion. Complexation depends on the available Cd cavity volume, as guest fitting variations and the enthalpy penalty from Cd deformation impact on the binding constants (promoting a reduction of up to 104). The often neglected Cd deformation plays, thus, an important role in the interaction behavior of larger cavity Cd-based systems, being crucial in carbohydrate-mediated recognition phenomena. It corresponds to an increase in energy of ca. 90 kJ mol-1 in the simpler analyzed model system.

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

Combining Cellulose and Cyclodextrins: Fascinating Designs for Materials and Pharmaceutics.

Cellulose and cyclodextrins possess unique properties that can be tailored, combined, and used in a considerable number of applications, including textiles, coatings, sensors, and drug delivery systems. Successfully structuring and applying cellulose and cyclodextrins conjugates requires a deep understanding of the relation between structural, and soft matter behavior, materials, energy, and function. This review focuses on the key advances in developing materials based on these conjugates. Relevant aspects regarding structural variations, methods of synthesis, processing and functionalization, and corresponding supramolecular properties are presented. The use of cellulose/cyclodextrin conjugates as intelligent platforms for applications in materials science and pharmaceutical technology is also outlined, focusing on drug delivery, textiles, and sensors.

Adsorption of charged macromolecules upon multicomponent responsive surfaces.

Adsorption of polyions onto charged surfaces has long been recognized as a crucial phenomenon in biological and technological applications. An intuitive model relating polyelectrolyte adsorption with the imposed features of polarizable surfaces of different compositions and charges is proposed based on Monte Carlo simulations using a coarse-grained approach. The excellent performance of the equation allows simultaneously describing a wide range of adsorption regimes and accounting for specific non-monotonic trends. For a constant surface charge density, the surface composition governs adsorption, promoting variations exceeding 100%. Adsorption increases with the number of attractive charges in the surface until reaching a maximum, decreasing thereafter due to the presence of polyanion-like charged particles. The presence of crowders hampers adsorption. These results can be used to efficiently predict and modulate the interaction between charged macromolecules and different substrates with direct implications in de novo designs of vehicles and biomedical devices.

Unstructured Formulation Data Analysis for the Optimization of Lipid Nanoparticle Drug Delivery Vehicles.

Designing nanoparticle formulations with features tailored to their therapeutic targets in demanding timelines assumes increased importance. In this context, nanostructured lipid carriers (NLCs) offer an excellent example of a drug delivery nanosystem that has been broadly explored in the treatment of glioblastoma multiforme (GBM). Distinct fundamental NLC quality attributes can be harnessed to fit this purpose, namely particle size, size distribution, and zeta potential. These critical aspects intrinsically depend on the formulation components, influencing drug loading capacity, drug release, and stability of the NLCs. Wide variations in their composition, including the type of lipids and other surface modifier excipients, lead to differences on these parameters. NLC target product profile involves small mean particle sizes, narrow size distributions, and absolute values of zeta potential higher than 30 mV. In this work, a wealth of data previously obtained in experiments on NLC preparation, encompassing, e.g., results of preliminary studies and those of intermediate formulations, is analyzed in order to extract information useful in further optimization studies. Principal component analysis (PCA) and partial least squares (PLS) are performed to evaluate the influence of NLC composition on the respective characteristics. These methods provide a rapid and discriminatory analysis for establishing a preformulation framework, by selecting the most suitable types of lipids, surfactants, surface modifiers, and drugs, within the set of investigated variables. The results have direct implications in the optimization of formulation and processes.

Exploring PAZ/3'-overhang interaction to improve siRNA specificity. A combined experimental and modeling study.

The understanding of the dynamical and mechanistic aspects that lie behind siRNA-based gene regulation is a requisite to boost the performance of siRNA therapeutics. A systematic experimental and computational study on the 3'-overhang structural requirements for the design of more specific and potent siRNA molecules was carried out using nucleotide analogues differing in structural parameters, such as sugar constraint, lack of nucleobase, distance between the phosphodiester backbone and nucleobase, enantioselectivity, and steric hindrance. The results established a set of rules governing the siRNA-mediated silencing, indicating that the thermodynamic stability of the 5'-end is a crucial determinant for antisense-mediated silencing but is not sufficient to avoid sense-mediated silencing. Both theoretical and experimental approaches consistently evidence the existence of a direct connection between the PAZ/3'-overhang binding affinity and siRNA's potency and specificity. An overall description of the systems is thus achieved by atomistic simulations and free energy calculations that allow us to propose a robust and self-contained procedure for studying the factors implied in PAZ/3'-overhang siRNA interactions. A higher RNAi activity is associated with a moderate-to-strong PAZ/3'-overhang binding. Contrarily, lower binding energies compromise siRNA potency, increase specificity, and favor siRNA downregulation by Ago2-independent mechanisms. This work provides in-depth details for the design of powerful and safe synthetic nucleotide analogues for substitution at the 3'-overhang, enabling some of the intrinsic siRNA disadvantages to be overcome.

Modeling of ultra-small lipid nanoparticle surface charge for targeting glioblastoma.

Surface modification of ultra-small nanostructured lipid carriers (usNLC) via introduction of a positive charge is hypothesized to prompt site-specific drug delivery for glioblastoma multiforme (GBM) treatment. A more effective interaction with negatively charged lipid bilayers, including the blood-brain barrier (BBB), will facilitate the nanoparticle access to the brain. For this purpose, usNLC with a particle size of 43.82 ±â€¯0.03 nm and a polydispersity index of 0.224 were developed following a Quality by Design approach. Monomeric and gemini surfactants, either with conventional headgroups or serine-based ones, were tested for the surface modification, and the respective safety and efficacy to target GBM evaluated. A comprehensive in silico-in vitro approach is also provided based on molecular dynamics simulations and cytotoxicity studies. Overall, monomeric serine-derived surfactants displayed the best performance, considering altogether particle size, zeta potential, cytotoxic profile and cell uptake. Although conventional surfactants were able to produce usNLC with suitable physicochemical properties and cell uptake, their use is discouraged due to their high cytotoxicity. This study suggests that monomeric serine-derived surfactants are promising agents for developing nanosystems aiming at brain drug delivery.

Computational modeling in glioblastoma: from the prediction of blood-brain barrier permeability to the simulation of tumor behavior.

The integrated in silico-in vitro-in vivo approaches have fostered the development of new treatment strategies for glioblastoma patients and improved diagnosis, establishing the bridge between biochemical research and clinical practice. These approaches have provided new insights on the identification of bioactive compounds and on the complex mechanisms underlying the interactions among glioblastoma cells, and the tumor microenvironment. This review focuses on the key advances pertaining to computational modeling in glioblastoma, including predictive data on drug permeability across the blood-brain barrier, tumor growth and treatment responses. Structure- and ligand-based methods have been widely adopted, enabling the study of dynamic and evolutionary aspects of glioblastoma. Their potential applications as predictive tools and the advantages over other well-known methodologies are outlined. Challenges regarding in silico approaches for predicting tumor properties are also discussed.

Hydrogel-Based Drug Delivery Nanosystems for the Treatment of Brain Tumors.

Chemotherapy is commonly associated with limited effectiveness and unwanted side effects in normal cells and tissues, due to the lack of specificity of therapeutic agents to cancer cells when systemically administered. In brain tumors, the existence of both physiological barriers that protect tumor cells and complex resistance mechanisms to anticancer drugs are additional obstacles that hamper a successful course of chemotherapy, thus resulting in high treatment failure rates. Several potential surrogate therapies have been developed so far. In this context, hydrogel-based systems incorporating nanostructured drug delivery systems (DDS) and hydrogel nanoparticles, also denoted nanogels, have arisen as a more effective and safer strategy than conventional chemotherapeutic regimens. The former, as a local delivery approach, have the ability to confine the release of anticancer drugs near tumor cells over a long period of time, without compromising healthy cells and tissues. Yet, the latter may be systemically administered and provide both loading and targeting properties in their own framework, thus identifying and efficiently killing tumor cells. Overall, this review focuses on the application of hydrogel matrices containing nanostructured DDS and hydrogel nanoparticles as potential and promising strategies for the treatment and diagnosis of glioblastoma and other types of brain cancer. Some aspects pertaining to computational studies are finally addressed.

Reconstructing the historical synthesis of mauveine from Perkin and Caro: procedure and details.

Mauveine, an iconic dye, first synthesised in 1856 still has secrets to unveil. If nowadays one wanted to prepare the original Perkin's mauveine, what would be the procedure? It will be described in this work and lies on the use of a 1:2:1 (mole) ratio of aniline, p-toluidine and o-toluidine. This was found from a comparison of a series of products synthesized from different proportions of these starting materials, with a set of historical samples of mauveine and further analysed with two unsupervised chemometrics methods.

Free-energy patterns in inclusion complexes: the relevance of non-included moieties in the stability constants.

Inclusion complexes play a definite role in a variety of applications, ranging from drug solubilization to smart materials. This work presents a series of studies based on molecular dynamics, including potential of mean force calculations, and aiming at understanding the factors that govern inclusion. Naphthalene and its derivatives are used as guests for a common host, ß-cyclodextrin. It is observed that the substitution of naphthalene promotes an increase in the complexation constant (up to 100-fold), irrespective of the nature of the substituent, the latter comprising small hydrophobic and hydrophilic (including charged) groups. It is also seen that entropy does not favor inclusion, the order of magnitude of the binding free energy being given by the enthalpic component, with a dominating guest-host interaction contribution. Desolvation penalizes the inclusion process, and is not observed in the vicinity of the hydrophilic and charged groups, which remain exposed to the solvent. Results suggest that substantial modulation of the inclusion complexes can be achieved imposing different substituents, with direct transposition for the modulation of properties in supramolecular structures based on these complexes.