OpenBabel online portal

OpenBabel online portal allows to convert nearly all the chemical formats and it very practical to quickly move from one program to another:
https://www.cheminfo.org/Chemistry/Cheminformatics/FormatConverter/index.html

Open Babel is a chemical toolbox designed to speak the many languages of chemical data. It’s an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.

The latest version of this documentation is available in several formats from:
https://openbabel.org/docs/index.html

Enjoy!!

How to estimate ADMET properties of a new drug

Estimating ADMET properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is critical in drug discovery and development to predict the behavior of a drug in vivo. Here’s a detailed approach to estimating these properties:

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1. Absorption

Absorption evaluates how effectively a drug enters systemic circulation after administration.

Key Factors:

• Solubility: Affects dissolution rate and bioavailability.
• Permeability: Indicates how well a drug crosses biological membranes.
• pKa: Determines ionization state, which influences absorption in different pH environments.

Methods of Estimation:

1. Experimental Techniques:

   • Caco-2 Cell Assay: Simulates intestinal epithelial permeability.
   • Parallel Artificial Membrane Permeability Assay (PAMPA): Assesses passive permeability.
   • Solubility Tests: Conducted in various pH buffers mimicking gastrointestinal conditions.

2. In Silico Models:

   • Lipinski’s Rule of Five: Predicts oral bioavailability using molecular weight, hydrogen bond donors/acceptors, and lipophilicity (logP).
   • QSAR Models (Quantitative Structure-Activity Relationships): Relate molecular features to absorption data.
   • GastroPlus®: Simulates gastrointestinal absorption.

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2. Distribution

Distribution assesses how a drug disperses throughout the body’s tissues and fluids.

Key Factors:

• Volume of Distribution (Vd): Indicates the extent of drug distribution.
• Plasma Protein Binding (PPB): Impacts free drug availability.
• Tissue Binding: Influences drug accumulation in specific organs.

Methods of Estimation:

1. Experimental Techniques:

   • Equilibrium Dialysis or Ultrafiltration: Measures plasma protein binding.
   • Animal Studies: Assess tissue-specific concentrations.

2. In Silico Models:

   • LogP and LogD Calculations: Predict lipophilicity, a key determinant of tissue affinity.
   • Predictive Algorithms (e.g., pkCSM, ADMET Predictor): Estimate Vd and PPB from chemical structure.

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3. Metabolism

Metabolism evaluates how a drug is chemically modified by enzymes, primarily in the liver.

Key Factors:

• Phase I Metabolism: Involves oxidation, reduction, or hydrolysis (e.g., by cytochrome P450 enzymes).
• Phase II Metabolism: Involves conjugation reactions like glucuronidation or sulfation.
• Metabolic Stability: Reflects the drug’s half-life in metabolic systems.

Methods of Estimation:

1. Experimental Techniques:

   • Liver Microsomes or Hepatocytes: Assess enzyme-mediated metabolism.
   • Cytochrome P450 Inhibition/Induction Studies: Identify potential drug-drug interactions.
   • Metabolite Identification: Using LC-MS/MS or NMR spectroscopy.

2. In Silico Models:

   • SMARTCyp: Predicts likely metabolic sites.
   • MetaSite and StarDrop: Simulate enzyme-substrate interactions.
   • Physiologically-Based Pharmacokinetic (PBPK) Models: Estimate drug clearance and metabolic pathways.

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4. Excretion

Excretion assesses how the drug and its metabolites are eliminated from the body.

Key Factors:

• Renal Excretion: Includes glomerular filtration, tubular secretion, and reabsorption.
• Biliary Excretion: Drug elimination via bile.
• Half-life (t½): Indicates the duration of drug action.

Methods of Estimation:

1. Experimental Techniques:

   • In Vitro Transporter Assays: Evaluate interaction with renal and hepatic transporters (e.g., OATs, OCTs, P-gp).
   • Animal Models: Measure urinary and fecal excretion.

2. In Silico Models:

   • Clearance Predictions: Based on molecular size, charge, and hydrophobicity.
   • Transporter Interaction Models: Predict transporter-mediated excretion pathways.

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5. Toxicity

Toxicity predicts adverse effects that a drug may induce.

Key Factors:

• Acute Toxicity: Evaluates single-dose lethality.
• Chronic Toxicity: Assesses long-term exposure effects.
• Organ-Specific Toxicity: Focus on liver, kidneys, and heart.
• Genotoxicity: Risk of DNA damage.
• Off-Target Effects: Interactions with unintended biological targets.

Methods of Estimation:

1. Experimental Techniques:

   • Cytotoxicity Assays: Using cell lines to assess viability (e.g., MTT, LDH assays).
   • hERG Assays: Test potential for cardiac arrhythmias.
   • In Vivo Studies: Evaluate systemic toxicity in animal models.

2. In Silico Models:

   • DEREK Nexus and ADMET Predictor: Assess structural alerts for toxicity.
   • T.E.S.T. (Toxicity Estimation Software Tool): Predicts various toxicity endpoints.
   • QSAR for Specific Toxicity: Models for mutagenicity, carcinogenicity, or reproductive toxicity.

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Integrated Approaches for ADMET Estimation

1. High-Throughput Screening (HTS):

   • Combines automated assays for multiple ADMET parameters.
   • Prioritizes compounds with favorable profiles.

2. In Silico Workflow:

   • Use cheminformatics platforms like Schrödinger’s QikProp or ADMETlab for rapid screening of ADMET properties.

3. PBPK Modeling:

   • Combines ADMET data with physiological parameters for holistic predictions of drug behavior in humans.

4. Machine Learning Models:

   • Utilize datasets of known drugs to predict ADMET properties based on chemical descriptors.

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Conclusion

By integrating experimental data with in silico predictions, researchers can efficiently estimate ADMET properties and prioritize promising drug candidates for further development. Balancing cost, speed, and accuracy is key to successful ADMET profiling.

Threadripper PRO 7000 WX-Series Processors

Interesting white paper published by AMD about Threadripper PRO 7000 WX-Series Processors, you can find the link to download it here:
Threadripper PRO 7000 WX-Series Processors

Enjoy!!

Why In-silico simulations are a great tool for discovery

In-silico simulations, which use computational models to predict biological and chemical processes, have become an integral part of pre-clinical drug development. Here are the key advantages:

1. Cost Efficiency

• Reduced Experimental Costs: Simulations help screen potential drug candidates before conducting expensive laboratory experiments.
• Minimized Animal Testing: By modeling drug behavior, in-silico methods reduce reliance on costly and ethically sensitive animal studies.

2. Time Savings

• Accelerated Drug Discovery: Computational models rapidly evaluate large compound libraries, identifying promising candidates much faster than traditional methods.
• Shortened Development Timelines: Simulations allow for simultaneous evaluation of multiple parameters, expediting hypothesis testing and optimization.

3. Enhanced Predictive Accuracy

• Molecular Modeling: Advanced algorithms predict drug-receptor interactions, guiding structural modifications for improved efficacy and safety.
• Pharmacokinetics and Dynamics: In-silico simulations forecast ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, reducing late-stage failures.

4. Customizable Scenarios 

• Parametric Analysis: Simulations can test drugs under various biological conditions, providing insights that might be challenging to replicate experimentally.
• Patient-Specific Modeling: Personalized medicine approaches benefit from in-silico predictions tailored to genetic or physiological variability.

5. Improved Risk Management

• Toxicity Screening: Early detection of adverse effects helps eliminate unsuitable candidates before clinical trials.
• Mechanistic Insights: Detailed simulations uncover the underlying mechanisms of action, reducing uncertainty in decision-making.

6. Scalability

• High-Throughput Screening: In-silico tools enable the evaluation of thousands of compounds in parallel, which would be impractical with physical testing.
• Global Collaboration: Cloud-based simulation platforms facilitate data sharing and collaborative research across institutions.

7. Environmental Benefits

• Reduction in Lab Waste: Fewer physical experiments mean less chemical waste, aligning with sustainable practices.
• Energy Efficiency: Computational methods often consume less energy compared to resource-intensive laboratory setups.

Conclusion

In-silico simulations bridge the gap between theoretical research and practical application, providing a powerful toolset for pre-clinical drug development. They not only optimize resources but also enhance the reliability of predictions, paving the way for more efficient and ethical drug discovery pipelines.

The impact of in-silico simulations on the pharmaceutical industry

In-silico simulations have revolutionized the pharmaceutical industry by significantly enhancing the efficiency, precision, and cost-effectiveness of drug discovery, development, and validation. Their impact can be characterized as follows:

1. Accelerated Drug Discovery: Computational models enable high-throughput virtual screening of vast chemical libraries against biological targets, identifying promising drug candidates with reduced reliance on labor-intensive experimental assays.

2. Rational Drug Design: Molecular dynamics simulations and quantum chemistry computations allow for the precise prediction of ligand-receptor interactions, guiding the optimization of binding affinities and pharmacokinetic properties.

3. Predictive Toxicology and Safety Assessment: In-silico models predict potential adverse effects and off-target interactions early in the drug development pipeline, minimizing late-stage failures and improving patient safety.

4. Clinical Trial Simulation: Virtual populations and pharmacometric modeling are used to simulate clinical trial outcomes, optimizing study designs, and enabling adaptive trial methodologies.

5. Cost and Time Efficiency: By reducing the need for extensive wet-lab experiments and animal testing, in-silico simulations lower R&D costs and shorten the timeline from concept to market.

6. Personalized Medicine: Computational approaches integrate patient-specific data, including genomics and proteomics, to predict individualized drug responses and guide tailored therapeutic strategies.

In summary, in-silico simulations have become indispensable in modern pharmaceutical innovation, driving a paradigm shift toward data-driven, efficient, and precision-focused drug development.

 

 

Symbolic Regression for in-silico simulations

Symbolic regression represents a transformative approach in in-silico computing, enabling systems to derive interpretable mathematical models from complex datasets. Unlike traditional machine learning, which often functions as a "black box," symbolic regression identifies explicit equations and relationships that describe underlying system dynamics. Here's how you might introduce its impact effectively:

Introduction to Symbolic Regression in In-Silico Computing

In the realm of in-silico computing, symbolic regression has emerged as a powerful tool to bridge the gap between raw data and meaningful insight. By leveraging advanced techniques, such as evolutionary algorithms and deep learning, symbolic regression discovers mathematical expressions that describe patterns, interactions, and causal relationships within computational models.

This capability has profound implications:

1. Enhanced Model Interpretability: Unlike conventional machine learning models, symbolic regression outputs human-readable equations, enabling scientists and engineers to understand and validate system behaviors.

2. Data-Driven Discovery: It facilitates hypothesis generation in fields ranging from materials science to bioinformatics by uncovering relationships that might remain hidden in traditional statistical approaches.

3. Optimization and Computational Efficiency: The compact equations derived from symbolic regression often reduce computational overhead compared to high-dimensional, opaque models.

4. Cross-Disciplinary Applications: From predicting physical phenomena to designing novel algorithms for artificial intelligence, symbolic regression has the versatility to transform workflows across disciplines.

By making in-silico systems more transparent and accessible, symbolic regression accelerates innovation and fosters collaboration between computational experts and domain scientists. As we continue to integrate this method into the broader landscape of scientific computing, its impact on understanding and simulating complex systems becomes increasingly indispensable.