Development of an a priori computational approach for brain uptake of compounds in an insect model system

Geldenhuys, Werner J.; Bloomquist, Jeffrey R.

doi:10.1016/j.bmcl.2021.127930

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…From the result of Table 3 , four compounds that allowed the best entry into the absorption of the central nervous system (CNS) through the blood–brain barrier and one that had excellent absorption properties through the membrane were selected, as shown in the graph (see Figure S2 in the supplementary material ), in which the white and yellow regions (yolk) are equivalent to the area most likely to be absorbed by the cell and most likely to access the brain (penetration), respectively. In agreement with Geldenhuys [ 90 ], who, in his study, developed a computational approach for the brain uptake of compounds in an insect model system, we concluded that in silico database filtering models of small compounds used in drug discovery were able to express the correlation between the BBB models. When analyzing the graph of the three growth-inhibiting insecticides on the market, DFB and BPU, together with compounds M01 , M02 , M03 , and M04, are found in the yellow region (yolk) [ 89 ].…”

Section: Resultssupporting

confidence: 88%

“…Since solubility is one of the main physicochemical properties for the development of a drug, the partition coefficient between n -octanol and water in its logarithmic form (log P o/w ) could determine the assessment of the pharmacological properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of a candidate chemical lead for its initial selection. It establishes benchmarks against which compounds synthesized during lead optimization can be evaluated [ 90 , 91 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Costa

Neto

Silva

et al. 2022

IJMS

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Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg·L−1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < −0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < −0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01–M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.

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Section: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Costa

Neto

Silva

et al. 2022

IJMS

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“…Conserved mechanisms between efflux transporters in insects and mammals are also supported by the transcriptomic identification of a human P-glycoprotein in locusts and the kinetic analysis of transport by a PgP substrate in the ex-vivo model [112]. Recently, machine-learning chemoinformatic models have been applied to predict the uptake of compounds into the ex-vivo locust brain [113]. Based on the published, and the availability of further, experimental data, the correlation of the ex-vivo locust with mammalian blood-brain barrier models appears satisfactory.…”

Section: Extrapolation From the Invertebrate Model To Humansmentioning

confidence: 98%

Looking at Developmental Neurotoxicity Testing from the Perspective of an Invertebrate Embryo

Bicker¹

2022

IJMS

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Developmental neurotoxicity (DNT) of chemical compounds disrupts the formation of a normal brain. There is impressive progress in the development of alternative testing methods for DNT potential in chemicals, some of which also incorporate invertebrate animals. This review briefly touches upon studies on the genetically tractable model organisms of Caenorhabditis elegans and Drosophila melanogaster about the action of specific developmental neurotoxicants. The formation of a functional nervous system requires precisely timed axonal pathfinding to the correct cellular targets. To address this complex key event, our lab developed an alternative assay using a serum-free culture of intact locust embryos. The first neural pathways in the leg of embryonic locusts are established by a pair of afferent pioneer neurons which use guidance cues from membrane-bound and diffusible semaphorin proteins. In a systematic approach according to recommendations for alternative testing, the embryo assay quantifies defects in pioneer navigation after exposure to a panel of recognized test compounds for DNT. The outcome indicates a high predictability for test-compound classification. Since the pyramidal neurons of the mammalian cortex also use a semaphorin gradient for neurite guidance, the assay is based on evolutionary conserved cellular mechanisms, supporting its relevance for cortical development.

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Section: Ligand-based Virtual Screeningmentioning

confidence: 99%

“…In other studies, the authors developed multiple algorithms to investigate QSAR [ 90 – 92 ]. For example, A study combined kNN, SVM, MLR, neural nets to predict the brain uptake ability of 25 known drugs obtained from PubChem [ 90 ]. Previously, seven methods (naïve Bayes classifier, Sequential Minimal Optimization–SMO, Instance-Based Learning, Decorate, Hyper Pipes, PART, and RF) have been conducted on the thirteen data sets (HIV-1 integrase inhibitors from ChEMBL database), the SMO was the highest efficiency to classify compounds [ 91 ].…”

Section: Ligand-based Virtual Screeningmentioning

confidence: 99%

Opportunities and challenges in application of artificial intelligence in pharmacology

et al. 2023

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Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

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Development of an a priori computational approach for brain uptake of compounds in an insect model system

Cited by 4 publications

References 18 publications

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Looking at Developmental Neurotoxicity Testing from the Perspective of an Invertebrate Embryo

Opportunities and challenges in application of artificial intelligence in pharmacology

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