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Overview
Explanation-Property
1. Physical properties 2. Absorption 3. Distribution 4. Metabolism 5. Excretion 6. Toxicity
Explanation-Transform
MMP Transformation Rule Global rules Local transformation Average change Variance Distribution Credibility Experimental database Expanded database
References

Tutorial

Overview

One of the great challenges in drug discovery is rationalizing lead optimization. In this phase, compounds that have been identified as the potential drug candidates need to be modified carefully with appropriate absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The key questions faced by medicinal chemists are which compound(s) should be made next and how to balance multiple properties. To avoid the poor or ill-advised decisions, it is crucial to draw credible transformation rules from multiple experimental assays and apply them for efficient optimization. Therefore, OptADMET, the first integrated chemical transformation rule platform covering 32 important ADMET properties, is able to provide multiple-property rules and apply invaluable experience for lead optimization. For multiple-property rule database, a total of 177,191 experimental data are used for analysis, which have produced 41,779 credible transformation rules. Besides, 239,194 accurate predicted molecular data are integrated with the initial data for expanded structural conversion exploration, which have converted 146,450 rules as the supplement. Based on the large and credible rule database, OptADMET is able to find desirable substructure transformation and guide the efficient multi-parameter optimization for the queried molecule. Additionally, to benefit the final decision, the ADMET profile of all optimized molecules will also be provided by OptADMET for comprehensive evaluation.

Fig 1. The constitute of OptADMET webserver

Fig 1. The constitute of OptADMET webserver

OptADMET webserver provides two functions: Database and Optimization.

Database

In the “Database” module, users can find the qualified chemical transformation of Single property or Multiple property, with requirements of data source, property and etc.

For Single Property transformation rules (Fig 2), users can click the picture to get more information, including ID, left fragment, right fragment, distribution, average change, the number of MMPs (count), statistical significance, local transformations summary table and experimental MMPs summary table. (Fig 3)

Fig 2. Single property filters

Fig 3. Detailed global rule information

By clicking the local transformation item more information about this local transformation will be provided, including ID, left fragment, right fragment, distribution, average change, the number of MMPs (count), statistical significance and experimental MMPs summary table. (Fig 4)

Fig 4. Detailed local rule information

For Multiple Property transformation rules, users can find the chemical transformation with the summary of multiple property information by selecting data source and property (maximum 2). (Fig 5)

Fig 5. Multiple property filters

By clicking the picture, more information about this rule will be provided, including ID, left fragment, right fragment, the property variance summary of the global rule and the property variance summary of the local transformation. (Fig 6)

Fig 6. Detailed rule information

Optimization

In the “Optimization” module, users can optimize the initial molecule with regards to a number of ADMET properties. Two molecule input types are provided: 1. input single SMILES string; 2. draw molecule from Editor below. Along with query molecule, users can choose transformation rules from experimental or expanded database, with maximum two properties. (Fig 7)

Fig 7. Checker module

After submission, the Summary and Generated block will present the overview of the optimization results and the detailed information about the chemical transformation rules. (Fig 8)

Fig 8. The result page of Optimization module

In the Summary block, the information of the original molecule and the distribution of the optimized molecules will be provided.

In the Generated molecule block, the detailed information about optimized molecule and related transformation rule will be provided. By clicking the picture, more information about this rule will be provided, including ID, left fragment, right fragment, the property variance summary of the global rule and the property variance summary of the local transformation. (Fig 9) Besides, to benefit the comprehensive evaluation, the ADMET profile of the optimized molecule will be provided by clicking the Detail. (Fig 10)

Fig 9. Checker results

Fig 10. The ADMET profile of the optimized molecule

To achieve the multi-property task, users can choose concerned ADMET properties for further screening. The empirical-based ADMET property interval is summarized in Table 1.

Table1. The data information about OptADMET database

Type Endpoint Property interval Unit
Basic LogD7.4 1≤value≤3 log mol/L
Basic LogP 0≤value≤3 log mol/L
Basic LogS -4≤value≤0.5 log mol/L
Absorption Caco-2 –5.15≤value log cm/s
Absorption Pgp-inhibitor 0≤value≤0.3 probability 
Absorption Pgp-substrate 0≤value≤0.3 probability
Absorption F (30%) 0≤value≤0.3 probability
Distribution PPB value＜0.9 probability
Distribution BBB 0≤value≤0.3 probability
Distribution vdss 0.04≤value≤20 L/kg
Metabolism CYP 1A2-inhibitor value≤0.5 probability
Metabolism CYP 2C19-inhibitor value≤0.5 probability
Metabolism CYP 2C9-inhibitor value≤0.5 probability
Metabolism CYP 2D6-inhibitor value≤0.5 probability
Metabolism CYP 2d6-substrate value≤0.5 probability
Metabolism CYP 3A4-inhibitor value≤0.5 probability
Excretion T1/2 value≤0.5 probability
Toxicity Ames 0≤value≤0.3 probability
Toxicity BCF value≤0.5 probability
Toxicity DILI value≤0.5 probability
Toxicity EC value≤0.5 probability
Toxicity EI value≤0.5 probability
Toxicity FDAMDD value≤0.5 probability
Toxicity H-HT-2 value≤0.5 probability
Toxicity hERG value≤0.5 probability
Toxicity IGC50 value≤0.5 probability
Toxicity NR-AhR value≤0.5 probability
Toxicity NR-AR value≤0.5 probability
Toxicity NR-AR-LBD value≤0.5 probability
Toxicity Respiratory value≤0.5 probability
Toxicity SR-ARE value≤0.5 probability
Toxicity SR-MMP value≤0.5 probability

Explanation-Property

1. Physical properties

LogS: The logarithm of aqueous solubility value. The first step in the drug absorption process is the disintegration of the tablet or capsule, followed by the dissolution of the active drug. Low solubility is detrimental to oral absorption, and early measurement of this property is of great importance in drug discovery.
Change explanation: Increase means the transformation from left to right will increase the value of LogS (△≥+0.3), while Decrease means the transformation from left to right will decrease the value of LogS (△≤-0.3). No change means there is no change of the value (-0.3<△<+0.3).


LogP: The logarithm of the n-octanol/water distribution coefficient. logP possess a leading position with considerable impact on both membrane permeability and hydrophobic binding to macromolecules, including the target receptor as well as other proteins like plasma proteins, transporters, or metabolizing enzymes.
Change explanation: Increase means the transformation from left to right will increase the value of LogP (△≥+0.3), while Decrease means the transformation from left to right will decrease the value of LogP (△≤-0.3). No change means there is no change of the value (-0.3<△<+0.3).


LogD7.4: The logarithm of the n-octanol/water distribution coefficients at pH=7.4. To exert a therapeutic effect, one drug must enter the blood circulation and then reach the site of action. Thus, an eligible drug usually needs to keep a balance between lipophilicity and hydrophilicity to dissolve in the body fluid and penetrate the biomembrane effectively. Therefore, it is important to estimate the n-octanol/water distribution coefficients at physiological pH (logD7.4) values for candidate compounds in the early stage of drug discovery.
Change explanation: Increase means the transformation from left to right will increase the value of LogD7.4 (△≥+0.3), while Decrease means the transformation from left to right will decrease the value of LogD7.4 (△≤-0.3). No change means there is no change of the value (-0.3<△<+0.3).


2. Absorption

Caco-2 Permeability: Before an oral drug reaches the systemic circulation, it must pass through intestinal cell membranes via passive diffusion, carrier-mediated uptake or active transport processes. The human colon adenocarcinoma cell lines (Caco-2), as an alternative approach for the human intestinal epithelium, has been commonly used to estimate in vivo drug permeability due to their morphological and functional similarities. Thus, Caco-2 cell permeability has also been an important index for an eligible candidate drug compound.
Change explanation: Increase means the transformation from left to right will increase the value of Caco-2 Permeability (△≥+0.3), while Decrease means the transformation from left to right will decrease the value of Caco-2 Permeability (△≤-0.3). No change means there is no change of the value (-0.3<△<+0.3).


Pgp inhibitor: The inhibitor of P-glycoprotein. The P-glycoprotein, also known as MDR1 or 2 ABCB1, is a membrane protein member of the ATP-binding cassette (ABC) transporters superfamily. It is probably the most promiscuous efflux transporter, since it recognizes a number of structurally different and apparently unrelated xenobiotics; notably, many of them are also CYP3A4 substrates.
Change explanation: Increase means the transformation from left to right will increase the risk of inhibition, while Decrease means the transformation from left to right will decrease the risk of inhibition. No change means there is no change.


Pgp substrate: As described in the Pgp-inhibitor section, modulation of P-glycoprotein mediated transport has significant pharmacokinetic implications for Pgp substrates, which may either be exploited for specific therapeutic advantages or result in contraindications.
Change explanation: Increase means the transformation from left to right will increase the risk of inhibition, while Decrease means the transformation from left to right will decrease the risk of inhibition. No change means there is no change.


F30%: The human oral bioavailability 30%. For any drug administrated by the oral route, oral bioavailability is undoubtedly one of the most important pharmacokinetic parameters because it is the indicator of the efficiency of the drug delivery to the systemic circulation.
Change explanation: Increase means the transformation from left to right will decrease the bioavailability, while Decrease means the transformation from left to right will increase the bioavailability. No change means there is no change.


3. Distribution

PPB: Plasma protein binding. One of the major mechanisms of drug uptake and distribution is through PPB, thus the binding of a drug to proteins in plasma has a strong influence on its pharmacodynamic behavior. PPB can directly influence the oral bioavailability because the free concentration of the drug is at stake when a drug binds to serum proteins in this process.
Change explanation: Increase means the transformation from left to right will increase the value of PPB (△≥+0.3), while Decrease means the transformation from left to right will decrease the value of PPB (△≤-0.3). No change means there is almost no change of the value (-0.3< △<+0.3).


VDss: Volume Distribution. The VD is a theoretical concept that connects the administered dose with the actual initial concentration present in the circulation and it is an important parameter to describe the in vivo distribution for drugs. In practical, we can speculate the distribution characters for an unknown compound according to its VD value, such as its condition binding to plasma protein, its distribution amount in body fluid and its uptake amount in tissues.
Change explanation: Increase means the transformation from left to right will increase the value of VDss (△≥+0.3), while Decrease means the transformation from left to right will decrease the value of VDss (△≤-0.3). No change means there is almost no change of the value (-0.3<△<+0.3).


BBB penetration: Drugs that act in the CNS need to cross the blood–brain barrier (BBB) to reach their molecular target. By contrast, for drugs with a peripheral target, little or no BBB penetration might be required in order to avoid CNS side effects.
Change explanation: Increase means the transformation from left to right will increase the BBB penetration, while Decrease means the transformation from left to right will decrease the BBB penetration. No change means there is no change.


4. Metabolism

CYP1A2/ CYP2C19/ CYP2C9/ CYP2D6/ CYP3A4 inhibitor and CYP2D6 substrate: Based on the chemical nature of biotransformation, the process of drug metabolism reactions can be divided into two broad categories: phase I (oxidative reactions) and phase II (conjugative reactions). The human cytochrome P450 family (phase I enzymes) contains 57 isozymes and these isozymes metabolize approximately two-thirds of known drugs in human with 80% of this attribute to five isozymes––1A2, 2C9, 2C19, 2D6 and 3A4. Most of these CYPs responsible for phase I reactions are concentrated in the liver.
Change explanation: Increase means the transformation from left to right will increase the risk of inhibition, while Decrease means the transformation from left to right will decrease the risk of inhibition. No change means there is no change.


5. Excretion

T1/2: The half-life of a drug is a hybrid concept that involves clearance and volume of distribution, and it is arguably more appropriate to have reliable estimates of these two properties instead.
Change explanation: Increase means the transformation from left to right will shorten half-life, while Decrease means the transformation from left to right will extend half-life. No change means there is no change.


6. Toxicity

H-HT-2: The human hepatotoxicity. Drug induced liver injury is of great concern for patient safety and a major cause for drug withdrawal from the market. Adverse hepatic effects in clinical trials often lead to a late and costly termination of drug development programs.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


hERG Blockers: The human ether-a-go-go related gene. The During cardiac depolarization and repolarization, a voltagegated potassium channel encoded by hERG plays a major role in the regulation of the exchange of cardiac action potential and resting potential. The hERG blockade may cause long QT syndrome (LQTS), arrhythmia, and Torsade de Pointes (TdP), which lead to palpitations, fainting, or even sudden death.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


DILI: Drug-induced liver injury (DILI) has become the most common safety problem of drug withdrawal from the market over the past 50 years.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


AMES Toxicity: The Ames test for mutagenicity. The mutagenic effect has a close relationship with the carcinogenicity, and it is the most widely used assay for testing the mutagenicity of compounds.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


FDAMDD: The maximum recommended daily dose provides an estimate of the toxic dose threshold of chemicals in humans.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


Skin Sensitization: Skin sensitization is a potential adverse effect for dermally applied products. The evaluation of whether a compound, that may encounter the skin, can induce allergic contact dermatitis is an important safety concern.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


Eye Corrosion/Irritation: Assessing the eye irritation/corrosion (EI/EC) potential of a chemical is a necessary component of risk assessment. Cornea and conjunctiva tissues comprise the anterior surface of the eye, and hence cornea and conjunctiva tissues are directly exposed to the air and easily suffer injury by chemicals. There are several substances, such as chemicals used in manufacturing, agriculture and warfare, ocular pharmaceuticals, cosmetic products, and household products which can cause EI or EC.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


Respiratory Toxicity: Among these safety issues, respiratory toxicity has become the main cause of drug withdrawal. Drug induced respiratory toxicity is usually underdiagnosed because it may not have distinct early signs or symptoms in common medications and can occur with significant morbidity and mortality. Therefore, careful surveillance and treatment of respiratory toxicity is of great importance.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


IGC50: 48 hour Tetrahymena pyriformis IGC50 (concentration of the test chemical in water in mg/L that causes 50% growth inhibition to Tetrahymena pyriformis after 48 hours). The unit of IGC50 is −log10[(mg/L)/(1000*MW)].
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


NR-AR: Androgen receptor (AR), a nuclear hormone receptor, plays a critical role in AR-dependent prostate cancer and other androgen related diseases. Endocrine disrupting chemicals (EDCs) and their interactions with steroid hormone receptors like AR may cause disruption of normal endocrine function as well as interfere with metabolic homeostasis, reproduction, developmental and behavioral functions.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


NR-AR-LBD: Androgen receptor (AR), a nuclear hormone receptor, plays a critical role in AR-dependent prostate cancer and other androgen related diseases. Endocrine disrupting chemicals (EDCs) and their interactions with steroid hormone receptors like AR may cause disruption of normal endocrine function as well as interfere with metabolic homeostasis, reproduction, developmental and behavioral functions.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


NR-AhR: The Aryl hydrocarbon Receptor (AhR), a member of the family of basic helix-loop-helix transcription factors, is crucial to adaptive responses to environmental changes. AhR mediates cellular responses to environmental pollutants such as aromatic hydrocarbons through induction of phase I and II enzymes but also interacts with other nuclear receptor signaling pathways.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


SR-ARE: Oxidative stress has been implicated in the pathogenesis of a variety of diseases ranging from cancer to neurodegeneration. The antioxidant response element (ARE) signaling pathway plays an important role in the amelioration of oxidative stress. The CellSensor ARE-bla HepG2 cell line (Invitrogen) can be used for analyzing the Nrf2/antioxidant response signaling pathway. Nrf2 (NF-E2-related factor 2) and Nrf1 are transcription factors that bind to AREs and activate these genes.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


SR-MMP: Mitochondrial membrane potential (MMP), one of the parameters for mitochondrial function, is generated by mitochondrial electron transport chain that creates an electrochemical gradient by a series of redox reactions. This gradient drives the synthesis of ATP, a crucial molecule for various cellular processes. Measuring MMP in living cells is commonly used to assess the effect of chemicals on mitochondrial function; decreases in MMP can be detected using lipophilic cationic fluorescent dyes.
Change explanation: Increase means the transformation from left to right will increase the risk of toxicity, while Decrease means the transformation from left to right will decrease the risk of toxicity. No change means there is no change.


Explanation-Transform

MMP: Matched molecular pairs. MMP is generally defined as a pair of compounds that can be interconverted by a well-defined chemical transformation at a single site, with the change between the pair elements referring to the transform, and the invariant feature referring to the context.


Transformation Rule: this term refers to the chemical transformation leading to a change in a pharmacological or physiochemical endpoint. For global rules, they need to attain enough MMPs (≥5 for labeled-data, ≥10 for consecutive data).


Global rules: Transformation rules which are summarized without considering the context information.


Local transformation: Transformations which are summarized based on the consideration of substructure change and localized atom information. There are a total of 10 types of localized atom, including aromatic/aliphatic carbon, aromatic/aliphatic nitrogen, aromatic/aliphatic oxygen, aromatic/aliphatic sulfur, halogen atoms and other.


Average change: The average property change value of the specific transformation.


Variance: The index of overall property change of a specific transformation. According to the direction and range of average change, three variance levels are provided: Increase (△>0 for labeled-data, △≥+0.3 for consecutive data), No change (△=0 for labeled-data, -0.3≤△≤+0.3 for consecutive data) and Decrease (△<0 for labeled-data, △≤-0.3 for consecutive data).


Distribution: The distribution of MMP value changes of a specific transformation. Three variance levels are provided: Increase (△≥0 for labeled-data, △≥+0.3 for consecutive data), No change (△=0 for labeled-data, +0.3≥△≥-0.3 for consecutive data) and Decrease (△<0 for labeled-data, -0.3≥△ for consecutive data).


Credibility: The index of the credibility of transformation rule. Two credibility levels are provided: High (passed the statistical significance test) and Qualified (didn’t pass the statistical significance test).


Experimental database: Transformation rules are summarized from the 178,197 experimental data which were collected from literature and databases.


Expanded database: Transformation rules are summarized from the combination of 178,197 experimental and 244,845 accurate predicted data, which are used as the supplementary guidance.


References

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  • [2] Griffen, E. , Leach, A. G. , Robb, G. R. , & Warner, D. J. Matched molecular pairs as a medicinal chemistry tool. J Med Chem, 2011, 54(22): 7739-7750.
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  • [4] Sushko, Y. , Novotarskyi, S. , Korner, R. , Vogt, J. , Abdelaziz, A. , & Tetko, I. V. Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process. J Cheminform, 2014, 6(1): 48.
  • [5] Dong, J. , Wang, N. N. , Yao, Z. J. , Zhang, L. , Cheng, Y. , Ouyang, D. , Lu, A. P. , & Cao, D. S. ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform, 2018, 10(1): 29.
  • [6] Xiong, G. , Wu, Z. , Yi, J. , Fu, L. , Yang, Z. , Hsieh, C. , Yin, M. , Zeng, X. , Wu, C. , Lu, A. , Chen, X. , Hou, T. , & Cao, D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res, 2021, doi: 10.1093/nar/gkab255.
  • [7] Fu, L. , Liu, L. , Yang, Z. J. , Li, P. , Ding, J. J. , Yun, Y. H. , Lu, A. P. , Hou, T. J. , & Cao, D. S. Systematic Modeling of logD7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis. J Chem Inf Model, 2020, 60(1): 63-76.
  • [8] Fu, L. , Yang, Z. Y. , Yang, Z. J. , Yin, M. Z. , Lu, A. P. , Chen, X. , Liu, S. , Hou, T. J. , & Cao, D. S. QSAR-assisted-MMPA to expand chemical transformation space for lead optimization. Brief Bioinform, 2021, doi: 10.1093/bib/bbaa374.