In vitro prediction of human intestinal absorption and blood–brain barrier partitioning: development of a lipid analog for micellar liquid chromatography

Vrieze, Mike De; Janssens, Pieter; Szücs, Roman; Eycken, Johan Van der; Lynen, Frédéric

doi:10.1007/s00216-015-8911-z

Cited by 23 publications

(20 citation statements)

References 70 publications

(74 reference statements)

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“…The equations along with the statistical validation coefficient are reported in Table 4. Indeed, taking into account either the properties of the analytes assumed as having zero atomic charges (equations (13) and (14)) or those of the analytes assumed as completely charged (equations (15) and (16)) resulted in a BBB passage predictive strength inferior to that obtained by using MLC indexes. Such conclusions are supported by the lower correlation coefficients obtained.…”

Section: Iam Indexes In Log Bb Predictionmentioning

confidence: 97%

Determination of in Vitro and in Silico Indexes for the Modeling of Blood–Brain Barrier Partitioning of Drugs via Micellar and Immobilized Artificial Membrane Liquid Chromatography

et al. 2017

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In the present work, 79 structurally unrelated analytes were taken into account and their chromatographic retention coefficients, measured by immobilized artificial membrane liquid chromatography (IAM-LC) and by micellar liquid chromatography (MLC) employing sodium dodecyl sulfate (SDS) as surfactant, were determined. Such indexes, along with topological and physicochemical parameters calculated in silico, were subsequently used for the development of blood-brain barrier passage-predictive statistical models using partial least-squares (PLS) regression. Highly significant relationships were observed either using IAM (r (n - 1) = 0.78) or MLC (r (n - 1) = 0.83) derived indexes along with in silico descriptors. This hybrid approach proved fast and effective in the development of highly predictive BBB passage oriented models, and therefore, it can be of interest for pharmaceutical industries as a high-throughput BBB penetration oriented screening method. Finally, it shed new light into the molecular mechanism involved in the BBB uptake of therapeutics.

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Section: Iam Indexes In Log Bb Predictionmentioning

confidence: 97%

Determination of in Vitro and in Silico Indexes for the Modeling of Blood–Brain Barrier Partitioning of Drugs via Micellar and Immobilized Artificial Membrane Liquid Chromatography

et al. 2017

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“…Pharmacokinetic properties and toxicities are predicted by ADMET, which can predict permeability for, BBB (blood-brain barrier), HIA (human intestinal absorption), P-glycoprotein Substrate/Inhibitor, renal organic cation transporter, etc. ADMET results suggest M10 has positive (+) attributes in Blood-Brain Barrier Permeability, human intestinal absorption, which suggests that ( Table 6 ) the molecule can be well absorbed in the human body (Ajay et al., 1999 ; De Vrieze et al., 2015 ). Inhibition and initiation of P-glycoprotein have been reported as the cause of drug-drug interactions (Chen et al., 2011 ).…”

Section: Resultsmentioning

confidence: 99%

Molecular modeling of potent novel sulfonamide derivatives as non-peptide small molecule anti-COVID 19 agents

Pradhan

Prasad

Sinha

et al. 2021

Journal of Biomolecular Structure and Dynamics

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19. The Sulfonamides groups have been widely introduced in several drugs, especially for their antibacterial activities and generally prescribed for respiratory infections. On the other hand, imidazole groups have the multipotency to act as drugs, including antiviral activity. We have used a structure-based drug design approach to design some imidazole derivatives of sulfonamide, which can efficiently bind to the active site of SARS-CoV-2 main protease and thus may have the potential to inhibit its proteases activity. We conducted molecular docking and molecular dynamics simulation to observe the stability and flexibility of inhibitor complexes. We have checked ADMET (absorption, distribution, metabolism, excretion and toxicity) and drug-likeness rules to scrutinize toxicity and then designed the most potent compound based on computational chemistry. Our small predicted molecule non-peptide protease inhibitors could provide a useful model in the further search for novel compounds since it has many advantages over peptidic drugs, like lower side effects, toxicity and less chance of drug resistance. Further, we confirmed the stability of our inhibitor-complex and interaction profile through the Molecular dynamics simulation study. Our small predicted molecule Communicated by Ramaswamy H. Sarma

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“…admetSAR). Thus, the following attributes are very important during drug development processes for any phytochemical entities 63,64 .…”

Section: In-silico Pharmacokinetics Drug-likeliness and Admet Profile...mentioning

confidence: 99%

Identification of Plant-Based Drug-like Molecules as Potential Inhibitors against hACE2 and S-RBD of SARS-CoV-2 using Multi-step Molecular Docking and Dynamic Simulation Approach.

Parihar

Sonia

Choudhary

et al. 2022

Preprint

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There has been considerable interest to develop effective antiviral drugs with substantial efficacy to the varying lineage of SARS-CoV-2. The plant-based bioactive molecules (phytochemicals) have proven to exhibit promising therapeutic efficacy and immune-modulatory effect due to their inherent broad-spectrum biological properties such as antioxidant, antiviral, and anti-inflammatory with minimal or no side effects. The SARS-CoV-2 infection is initiated upon recognition and binding of the spike (S) Receptor-Binding Domain (RBD) to the host cell surface receptor, human Angiotensin-Converting Enzyme 2 (hACE2). Therefore, the underlying mechanism of interaction between host cell receptors and blocking the virus-cell interaction is considered to be a promising approach for the management and treatment of COVID-19 disease. In the present study, In-silico screening of phytochemicals against two targets of SARS-CoV-2 using a multi-step molecular docking approach was investigated. Based on the Glide-XP docking score, the top 5 molecules were subjected to steered molecular dynamics (SMD) simulation for calculation of binding force, and work done to get mechanistic insight has been carried out. Further, the op 3 ligands with the highest force and work were subjected to molecular dynamics simulation and binding free energy evaluations. The results revealed that the protein-ligand complexes showed stable trajectories throughout the 100 ns simulation. Moreover, the drug likeliness predicted by pKi, LE, ADMET analysis, and Pa & Pi values suggested that the best 3 lead molecules for both the targets (ACE2: Rosavin, Isoorientin, Palasitrin) & (RBD: Cinnamtannin B1, Isoskimmiwallin, Terflavin A) have good inhibitory efficiency, better pharmacokinetics, and are non-toxic under physiological conditions. Thus, these molecules can be used as potential therapeutic drugs against SARS-CoV-2 infection.

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In vitro prediction of human intestinal absorption and blood–brain barrier partitioning: development of a lipid analog for micellar liquid chromatography

Cited by 23 publications

References 70 publications

Determination of in Vitro and in Silico Indexes for the Modeling of Blood–Brain Barrier Partitioning of Drugs via Micellar and Immobilized Artificial Membrane Liquid Chromatography

Determination of in Vitro and in Silico Indexes for the Modeling of Blood–Brain Barrier Partitioning of Drugs via Micellar and Immobilized Artificial Membrane Liquid Chromatography

Molecular modeling of potent novel sulfonamide derivatives as non-peptide small molecule anti-COVID 19 agents

Identification of Plant-Based Drug-like Molecules as Potential Inhibitors against hACE2 and S-RBD of SARS-CoV-2 using Multi-step Molecular Docking and Dynamic Simulation Approach.

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