Drug Tissue Distribution Prediction Service
In traditional drug development, predicting drug distribution properties relies heavily on in vitro and in vivo studies. Despite advances in technological innovation, traditional experimental evaluation of drug distribution properties is often costly and time-consuming. As a leading computer-aided drug design (CADD) service provider, CD ComputaBio focuses on using artificial intelligence to accurately and quickly predict the distribution of drugs in various tissues in the body. This critical information helps pharmaceutical companies optimize the efficacy and safety of drugs and accelerate drug development timelines.
Introduction to Drug Tissue Distribution
Drug tissue distribution is an important process in pharmacokinetics because it can affect the amount of drug reaching the active site as well as the drug's efficacy and safety. The main reasons for the failure of 90% of drugs in clinical development are insufficient efficacy and uncontrolled toxicity. The prediction of drug distribution properties can help significantly reduce the time and cost of screening non-ideal drug candidates. Traditional in vivo studies used to characterize the distribution volume of clinical drug candidates are prone to errors, time-consuming and labor-intensive, and lack reproducibility in the clinical setting. In addition, in vitro screening of compounds is usually limited to a few properties and focuses only on a few of the most promising candidate compounds.
Fig. 1 General structure of a drug distribution property prediction model using AI. (Tran TTV, et al.; 2023)
However, instead of limiting the examination of distribution properties to a few specific molecules, artificial intelligence systems can efficiently and cheaply screen thousands of candidate molecules. Extensive use of computer simulations to quickly and early predict the distribution properties of drugs before further in vitro studies can help screen ideal candidate molecules. Such methods not only break through the limitations of traditional in vitro and in vivo experiments but also accurately predict the behavior of candidate drugs in vivo in large-scale screening.
Our Services
At CD ComputaBio, we use state-of-the-art computational methods to predict the tissue distribution of drugs with high accuracy and efficiency. Combined with our pharmacokinetic (PK) parameters and drug toxicity prediction services, we aim to streamline the drug development process for you and save time and resources for pharmaceutical companies. Our list of services covers:
Physiologically Based Pharmacokinetic (PBPK) Modeling
Based on anatomical, physiological, and biochemical parameters, a systemic PBPK model is constructed to predict the ADME process of drugs in various tissues and organs in the body.
Tissue Partition Coefficient (Kp) Prediction
Utilizing the prediction model to evaluate the distribution coefficient of drugs in different tissues and optimize the route of administration and dosage.
Permeability and Absorption
Prediction
Simulating the ability of drugs to pass through cell membranes and tissue barriers, and accurately predict the absorption and distribution characteristics of drugs.
Apparent Distribution Volume (Vd) Estimation Prediction
Predicting the Vd of the drug, understanding its distribution range and extent in the body, and helping customers determine the appropriate dosage and dosing regimen.
Specific Tissue Distribution
Prediction
Simulating the distribution of drugs in specific tissues, such as liver, kidney, lung, heart, brain, placenta, and tumor tissues to support drug development for specific diseases or target organs.
Blood-Brain Barrier Permeability Prediction
Predicting the ability of drugs to cross the blood-brain barrier and enter the central nervous system to support drug development for the treatment of neurological diseases.
Our Solutions
CD ComputaBio provides drug tissue distribution prediction services that focus on the following different types of drug molecules, combining their unique physicochemical and biological properties to accurately predict tissue distribution. In this way, we can help you identify potential risks before the first dose and optimize drug development strategies.
Small Molecule Drugs
Based on the physicochemical properties prediction results, such as solubility, hydrophobicity (Log P), acid-base dissociation constant (pKa), etc., its distribution characteristics are predicted. On this basis, the classic PK model is used to combine plasma protein binding rate and metabolic rate to predict the drug concentration in tissues.
Protein Drugs
Our scientists will assist you in analyzing the expression and distribution of target receptors and predicting the accumulation of drugs in different tissues. For antibody drugs, we will also consider the effects of neonatal Fc receptor (FcRn), antibody subtype, glycosylation pattern, etc., on its clearance and tissue distribution.
Nucleic Acid Drugs
Using machine learning algorithms such as deep learning, random forests, support vector machines, etc., we build a tissue distribution prediction model for nucleic acid drugs. Based on the sequence information, chemical modification, physicochemical properties, and delivery carrier characteristics of nucleic acid drugs, we predict their tissue distribution, biological stability, and targeting in vivo.
Drug-Loaded Nano-Delivery System
Based on the effects of nanoparticle size, shape, surface charge, and surface modification, such as PEGylationand, and targeted ligand modification, on in vivo distribution, we predict its ability to circulate in the blood and penetrate physiological barriers such as the blood-brain barrier, intestinal mucosal barrier, and tumor vascular wall, thereby clarifying its distribution behavior in specific tissues or lesions.
Service Highlights
Our team comprises experts with diverse backgrounds in computational biology, bioinformatics, and drug discovery, allowing us to offer comprehensive insights and solutions across various domains of research and development.
We understand that each research project is unique. Therefore, we tailor our services to meet the specific requirements and objectives of our clients, ensuring a customized approach that delivers maximum value.
By leveraging computational approaches, we offer cost-effective and time-efficient solutions, accelerating the drug development process and reducing the risks associated with experimental trial and error.
CD ComputaBio is dedicated to providing excellent drug tissue distribution prediction services to global pharmaceutical companies. Our rich professional experience, advanced technology platform, and personalized solutions give us a unique competitive advantage in the field of drug tissue distribution prediction. Please don't hesitate to contact us, if you are interested in our services.
References:
- Tran TTV, et al. Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction. International Journal of Molecular Sciences. 2023; 24(3):1815.
- Antontsev V, et al. A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform. Sci Rep. 2021; 11:11143.
* For Research Use Only.
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