ADMET Predictor 10.0 Brochure-2021
What is ADMET Predictor?
ADMET Predictor is a software tool that quickly and accurately predicts over 175 properties including solubility, logP, pKa, sites of CYP metabolism, and Ames mutagenicity. The ADMET Modeler™ module in ADMET Predictor allows one to rapidly and easily create high quality QSAR/QSPR models based on your own data. The newest module offers advanced data mining, clustering, and matched molecular pair analysis. The program has an intuitive user interface that allows one to easily manipulate and visualize data.
The eight modules in ADMET Predictor are:
Physicochemical and Biopharmaceutical (PCB)
Metabolism
Toxicity
ADMET Modeler™
MedChem Studio™
HTPK Simulation
AI-Driven Drug Design (AIDD)
Transporters
Each module can be purchased individually.
What are we providing with ADMET Predictor (AP)?
The ADMET Predictor development team strives to build the best QSAR/QSPR models and fast, efficient software. This dedication to science is a major reason why several of our models have ranked #1 in peer review articles. ADMET Predictor provides:
Over 175 predicted properties
Model applicability domain
pKa prediction including all microstates
CYP metabolite generation and kinetic parameters
Confidence estimates for classification models
A revamped, intuitive interface with pulldown menus, quick access icons, and context specific menus
Interactive distribution and 2D and 3D scatters plots
Ability to create your own QSPR/QSAR models
Excellent customer support
How is AP being applied?
Biotechnology, pharmaceutical, and chemical companies license ADMET Predictor for diverse number of applications including:
Physicochemical property prediction of real and virtual compounds
Metabolite prediction
Toxicity prediction
Prediction of dose needed to achieve a specific blood level concentration
Analysis of high throughput screening data
Matched molecule pair analysis and activity cliff detection
SAR analysis including R-group creation and analysis
Similarity searching
Creating diverse compounds subsets
Enumerating combinatorial chemistry libraries
Calculation of various binding metrics, e.g. lipophillic ligand efficiency (LLE)
ADMET Risk™
The original Rule of 5 is widely considered to be an important development in modern drug discovery (Lipinski, et al; 1997). The Rule of 5 takes on numeric values from 0 to 4 as a measure of the compounds potential absorption liability. As such, the Rule of 5 is a useful computational filter in drug candidate screening. In terms of ADMET Predictor descriptors and models, the Rule Of 5 model rules can be formulated as follow the following set of conditions:
MlogP > 4.15 (excessive lipophilicity)
MWt > 500 (large size)
HBDH > 5 (too many potential hydrogen bond donors)
M_NO > 10 (too many potential hydrogen bond acceptors)
Most commercial drugs suitable for oral dosing violate no more than one of the rules these conditions represent.
As an extension of that concept, Simulations Plus has created a series of “ADMET Risk” rule sets and calibrated them against our own ADMET models. They are parameterized to include thresholds for a wide range of calculated and predicted properties that represent potential obstacles to a compound being successfully developed as an orally bioavailable drug. These thresholds were obtained by focusing in on a specific subset of drugs in the World Drug Index (WDI). Similar to the methodology used by Lipinski et al., we removed irrelevant compounds from a 2008 version of the WDI. In particular, we removed phosphates, antiseptics, insecticides, emollients, laxatives, etc., as well as any compound that did not have an associated United States Adopted Name (USAN) or International Non-proprietary Name (INN) identifier. The structure of the principal component in salts was extracted and neutralized, after which duplicate structures were removed. This left us with a data set of 2,316 molecules, 8.3% of which violated more than one of Lipinski’s rules.
Rule of 5 only addresses a narrow slice of the full gamut of hurdles a compound must pass before it can become a drug. In addition, it relies on “hard” thresholds: a compound with a molecular weight of 499 satisfies the MWt rule but a compound with a molecular weight of 501 violates it.
We calculated a broad range of relevant molecular descriptors and ADMET property predictions for the focused subset of WDI and identified “soft” threshold ranges for each along the lines suggested by (Petit; 2012) such that approximately 85% of the compounds in the data set satisfy them completely and somewhat less than 10% violate them completely. The former contribute nothing to the overall Risk, whereas the latter contribute the full amount (weight) specified for the corresponding rule. Predictions falling in the gray area in between contribute fractional amounts to the Risk Score. The concept is illustrated on the left.
Highly correlated criteria were combined into single rules using Boolean operators. The rules for identifying overly large structures, for example, is:
size (Sz): MWt > [450,550] OR N_Atoms > [32,38] OR MolVol > [475,550] OR N_Bonds > [35,41]
where the values within the brackets indicate the boundaries of threshold regions. The Sz rule includes four individual criteria, all of which use the “>” relational operator. Predictions falling below the minimum threshold values contribute nothing to the Risk, whereas predictions above the maximum contribute 1 violation “point”. Intermediate values represent intermediate risks: a compound of molecular weight 500 violates the first criterion and so would represent an incremental Risk of 0.5 points for that criterion. Logical operators such as ORs and ANDs can also be included in the rules. The combined points from the criteria making up a rule then yield an overall value between 0 and 1, which is multiplied by the weight assigned to the rule as a whole.
The overall ADMET_Risk is the sum of three risks:
Absn_Risk – risk of low fraction absorbed (PCB Module models)
CYP_Risk – risk of high CYP metabolism (MET Module models)
TOX_Risk – toxicity related risks (TOX Module models)
Two additional pharmacokinetic risks (high plasma protein binding and high steady-state volume of distribution) are also included in the ADMET_Risk score.
References
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. “Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings.” Adv Drug Delivery Rev. 1997; 23:3-25.
Petit J, Meurice N, Kaiser C, Maggiora G. “Softening the Rule of Five – where to draw the line?” Bioorg Med Chem. 2012; 20:5343-5351.
ADMET Predictor 10.0 Brochure-2021
Physicochemical & Biopharmaceutical (PCB)
What is the PCB Module?
ADMET Predictor’s PCB Module contains models for physicochemical property prediction. Each model was built by Simulations Plus scientists using artificial neural network ensemble (ANNE) technology. The data sets were highly curated in order to generate highly accurate models. The data for our pKa model (S+pKa) was recently expanded in a collaboration with Bayer scientists. This resulted in more accurate predictions for a set of 16,000 compounds that were not used to train the model. Our logP (S+logP) and aqueous solubility (S+Sw) models have been ranked number one in peer reviewed journal articles.1,2 The models in the PCB module are also available in the ADMET Predictor module of GastroPlus® in order to perform PBPK simulations using in silico predicted properties.
Physicochemical property prediction
The image below lists the models in ADMET Predictor’s PCB Module.
The supersaturation (SupSatn), blood brain barrier penetration classification model (BBB_Filter), Pgp substrate (Pgp_Substr) and inhibition (Pgp_Inhib), and OATP1B1 inhibition (OATP1B1_Inh) models were rebuilt to improve their estimates of prediction confidence
Several new models based on data from the Extended Clearance Classification System journal article by Varma et al.3 were created for ADMET Predictor version 9.0
A model to prediction high or low permeability in a low efflux MDCK assay (S+MDCK-LE)
The Extended Clearance Classification System (ECCS) has been implemented
S+CL_Mech model predicts if a compound’s major clearance pathway is renal, metabolic, or hepatic uptake
S+CL_Renal, S+CL_Metab, S+CL_Uptake are binary classification models that predict if a compounds major clearance pathway is renal, metabolic, or hepatic uptake
The human plasma protein binding (hum_fup%) model was rebuilt with additional data
Plots of solubility and logD vs. pH
BCS4/DCS5 explorer window
1 J. Pharm. Sci. 2008, 98, 861.
2 a) Expert Opin. Drug Discov. 2006, 1, 31-52. b) Science of the Total Environment, 2013, 463-464, 781-789.
3 Varma MV, Steyn SJ, Allerton C, El-Kattan AF. “Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).” Pharm Res 2015; 32:3785.
4 Amidon, GL, Lennernas H, Shah VP, Crison JR”A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability.” Pharm. Res. 1995; 12:413-20.
5 Butler JM and Dressman JB. “The developability classification system: application of biopharmaceutics concepts to formulation development.” J. Pharm. Sci. 2010; 99(12):4940-54.
Brief descriptions of the models are below.
pKa, partition, and transporters models
Multiprotic pKa model (S+Acidic_pKa, S+Mixed_pKa, S+Basic_pKa) – a thermodynamically accurate multiprotic model for multiple ionization sites based on atomic descriptors and neural networks – not a database lookup!
logP (S+logP, MlogP) – log of the octanol to water partition coefficient. There are two models, an artificial neural network ensemble (S+logP) and Moriguchi (MlogP)
logD (S+logD) – estimation of octanol-water distribution coefficient at user-defined pH
Air-water partition (logHLC) – estimation of air-water partition coefficient (Henry’s Law constant) from US EPA data
Inhibition of the hepatic OATP1B1 transporter in human (OATP1B1_Inh)
Inhibition of the intestinal P-gp transporter in human (Pgp_Inh)
Likelihood of intestinal efflux by P-gp transporter in human (Pgp_Substr)
Permeability models
Human effective permeability (S+Peff) – jejunal Peff
MDCK apparent permeability (S+MDCK) – in vitro Papp
Corneal permeability (Perm_Cornea) – ocular permeability through rabbit cornea based on literature data obtained in vitro
Skin permeability (Perm_Skin) – permeability through human skin of compounds dissolved in aqueous solution; based on literature data
Blood-brain barrier permeation – there are two models, classification (BBB_Filter) and regression (LogBB). The first classification model has a low cutoff such that compounds that are predicted to have low permeability have very little chance of penetrating the blood-brain barrier. Compounds that are predicted to have high blood-brain barrier penetration should be evaluated with the regression model that predicts the blood to brain concentration ratio.
Solubility models
Aqueous solubility:
Native solubility (S+Sw) – solubility in pure water
Native pH at saturation in pure water (S+pH_Satd)
Intrinsic solubility in pure water (S+S_Intrins)
Salt solubility factor (SolFactor)
Water solubility at user-specified pH (S+S_pH)
Solubility in the simulated gastrointestinal fluids (the models were built using data in our Biorelevant Solubility Database)
Fasted state simulated gastric fluid solubility (S+FaSSGF)
Fasted state simulated intestinal fluid (S+FaSSIF)
Fed state simulated intestinal fluid (S+FeSSIF)
Supersaturation ratio (SupSatn) – a tendency to supersaturate in water
Pharmacokinetic models
Human and rat plasma protein binding as percent unbound (hum_fup% and rat_fup%
Human volume of distribution (Vd)
Human and rat blood-to-plasma concentration ratio (RBP and RBP_rat)
Fraction unbound in human liver microsomes (S+fumic)
重点文献推荐
1.【软件基本介绍】：详细讲述ADMETPredictor软件如何构建ADMET模型、软件操作及相关应用
In Silico Methods for Predicting DrugToxicity
【摘要】：药物发现与开发高投入、周期长的高风险项目，在过去几十年里，计算机辅助工具以及计算机虚拟预测insilico模型已被引入到药物发现阶段，用于预测化合物的ADMET (吸收、分布、代谢、排泄和毒性)性质特征，从而极大程度上避免药物研发后期由于代谢性质差和毒副作用大的失败风险。现在，研究人员已经广泛认识到应在药物发现阶段尽早熟知化合物的ADMET性质及可能存在的问题。在本书中，作者详细描述了商业软件程序ADMETPredictor™ 7.2 (ADMET Predictor v7.2. Simulations Plus, Inc., Lancaster, CA,USA)是如何构建ADMET模型以及如何采用建立好的模型预测化合物的ADMET性质参数。全章节分为背景、材料、方法三个部分，详细介绍了ADMETPredictor如何搭建内建模型、如何搭建用户自己的模型以及软件的基本操作和应用等内容。
2.【自建模型的应用】：大环内酯类化合物结构-色谱保留时间的模型搭建，用于有关物质分析方法的评价与新杂质的色谱行为的预测
Identification of impurities in macrolides by liquidchromatography-mass spectrometric detection and prediction of retention timesof impurities by constructing quantitative structure-retention relationship(QSRR)
【摘要】：大环内酯类是一种多组分的药物，建立一个良好灵敏度和高选择性的分析方法进行杂质控制一直是较大的挑战。文章建立了三种独立、灵敏、准确的液相色谱质谱联用(LC-MS)方法以检测商业产品中的16环大环内酯类化合物(交沙霉素、交沙霉素丙酸酯、麦迪霉素醋酸酯)及其相关物质。
作者归纳总结了大环内酯类杂质特点的规则，这对分析该类药物的杂质情况是非常有用的指导资料。对于各个药物，采用高灵敏度的MS检测器检测大量未知的组成，并且基于所总结16环大环内酯的片段规则推测可能的结构。
采用了多重线性回归的方法构建了定量结构-色谱保留关系模型(QSRR)，以预测采用LC-MS方法未检测出、且没有获得标准品的杂质色谱保留时间。模型适用性高，其交叉验证的预测能力(Q2)为0.95。建立的模型的一般预测能力进一步通过外部预测准确性(平均预测误差是2.3%)得到了验证。结合8个分子描述符构建了最优的QSRR模型，且该模型展现了较好的预测精准性和稳定性。
3.【生物药物学参数预测准确性】：评估了不同软件对药物生物药剂学参数的预测准确性，发现ADMETPredictor对pKa预测最为准确
Experimental versus theoretical log D7.4 , pKa and plasmaprotein binding values for benzodiazepines appearing as new psychoactivesubstances
4.【法规部门软件应用】：EPA采用软件评估对雌激素代谢产物的预测准确性
Prediction of Estrogenic Bioactivity of EnvironmentalChemical Metabolites
CarolineL. Pinto, Kamel Mansouri, Richard Judson, Patience Browne. Chem Res Toxicol.2016 Sep 19; 29(9): 1410-27.
【摘要】：美国环保署EPA的内分泌干扰物筛选项目(EDSP)采用ToxCast/Tox21高通量筛选实验所产生的体外数据，以评估环境化学品的内分泌活性。
5.【毒性参数预测准确性评估】：评估不同软件对化合物毒性预测的准确性
Assessment of in silico models for acute aquatic toxicitytowards fish under REACH regulation
CappelliCI, Cassano A, Golbamaki A, Moggio Y, Lombardo A, Colafranceschi M, BenfenatiE. SAR QSAR Environ Res. 2015 Dec;26(12):977-999.
【摘要】：文章评估8种QSAR模型软件(ACD/ToxSuite™,ADMET Predictor™, DEMETRA, ECOSAR, TerraQSAR™, Toxicity Estimation SoftwareTool, TOPKAT™和VEGA)对两个种属鱼(黑头呆鱼和虹鳟鱼)的急性水生生物毒性的预测能力。
6.【成药性评估】：通过对化合物ADMET性质参数和风险risk预测，评估与筛选化合物
Design, Synthesis and Biological Evaluation of NovelBenzothiazole Derivatives as Selective PI3Kβ Inhibitors
ShuangCao, Ruiyuan Cao, Xialing Liu, Xiang Luo, Wu Zhong. Molecules. 2016 Jul 2;21(7).
【摘要】：设计与合成了一系列全新的含有苯并噻唑结构的PI3Kβ (磷脂酰环己硫醇-3-beta 激酶亚族)抑制剂。评估所有化合物均对PI3Kα,β, γ, δ和mTOR (哺乳动物雷帕霉素靶点)的抑制活性。根据抑制PI3Ks/mTOR的IC50数值筛选出两个较为不错的化合物做进一步的评估。
7.【ADME应用案例】：评估高通量筛选中影响化合物清除率与暴露的不确定因素
Evaluating the Impact of Uncertainties in Clearance andExposure When Prioritizing Chemicals Screened in High-Throughput AssaysEnvironSci Technol. 2016 Jun 7;50(11):5961-71.
【摘要】：毒性测试模式逐渐引入到高通量筛选方法中，以快速筛选成千上万的化合物。在高通量风险评估中一般依次选用体外筛选实验、计算机虚拟预测insilico方法预测体内暴露浓度与药代动力学PK行为的关系。
对预测的体内暴露或者PK行为的潜在不确定将显著影响化合物的往后发展的顺序，虽然这些影响因素暂时还未知。本研究中，建立了一套独立评估化合物优选顺序的整体框架，整合吸收剂量、PK性质、体外剂量-效应数据到PK/PD模型中。
将文献报道里预测的清除速率和吸收剂量数值整合到PK/PD模型中，进一步评估对化合物优选顺序的不确定影响因素。与文献报道的数值相比，采用预测的吸收剂量会比预测的清除速率数值具有更大的误差。最后，对可能影响不确定毒理活性的预测参数设置了可能的变化范围，并在模型中进行了参数敏感性的分析，以寻找潜在的原因。
8.【溶解度预测的比较】：化合物水中溶解度的计算预测
In silico prediction of aqueous solubility.
Dearden JC. (2006) Expert Opin. Drug Discov. 1:31-52
【摘要】：本文对化合物的水溶解度及其影响因素进行了简要概述，并简短介绍了定量结构-性质关系。
汇总了1990年以前关于水溶性预测的工作，并对1990年以后的大多数水溶性计算预测方法进行了详细介绍和深入讨论。1990年以后的水溶性计算预测方法开始使用具有多样性的训练集。
本文用21个药物和杀虫剂组成的测试集对这些研究中提及的水溶解度预测模型的准确性进行了验证。最后，我们用122个水溶解度已知的药物分子，对15款商业化软件的水溶解度预测准确性进行了评测。
9.【LogP预测的比较】：分子亲脂性的计算:主流Log P预测方法并用超过96,000个化合物对这些方法进行比较研究
Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds.
Mannhold R, Poda GI, Ostermann C, Tetko IV. (2008) J. Pharm. Sci. 98(3):861-93
【摘要】：Log P的预测方法可以分为两大类：一是基于结构，一是基于性质。我们首先回顾这两大类方法的最新研究进展。
然后，我们用一个公开数据集（N=226）和两个分别来自Nycomed（N=882）和Pfizer（N=95809）的内部数据集对这两大类预测方法中的代表性方法的预测能力进行了比较。用公开数据集比较了30种预测方法，用工业数据集比较了18中预测方法。所有模型的预测准确性随着非氢原子数目的增加而降低。算术平均模型（AAM）被用来作为比较的基准模型。如果模型的均方根误差（RSME）大于AAM的均方根误差，那么此方法是不可接受的。
对于公开数据集，大多数方法都能给出比较合理的结果；但是，对于内部数据集，只有七个方法能够同时对Nycomed和Pfizer的化合物进行成功预测。我们提出了一种基于碳原子（NC）和杂原子数目（NHET）的简单log P计算公式：log P = 1.46（±0.02）+ 0.11（±0.001）NC- 0.11（±0.001）NHET。该计算方法的表现优于大多数本研究中所涉及的方法。最后，本文对影响的log P预测准确性的因素进行了阐述和讨论。
10.【成药性筛选】：利用生物电子等排原理设计新的monastrol衍生物，并利用计算得到的性质参数进行ADMET性质预测
Bioisosteric approach in designing new monastrol derivatives: An investigation on their ADMET prediction using in silico derived parameters.
Hassan SF, Rashid U, Ansari FL, Ul-Haq Z. (2013) J Mol Graph Model. 45C:202-210
【摘要】：为了提供更安全、更有效的药物，药物化学家面临的挑战正变得越来越大。如何在各类药性质如溶解度、渗透性、代谢稳定性、药效和毒性之间取得恰当平衡，是在潜在候选药物的优化过程最具挑战性的问题之一。
难溶和渗透性差的化合物会导致酶实验和细胞实验产生错误的生物学数据和不可靠的SAR结果。Monastrol是第一个报道的有丝分裂驱动蛋白Eg5 小分子抑制剂；但由于其抑制活性弱、类药性差，从而阻碍了其进一步的发展。
本研究，首先，利用生物电子等排方法，得到monastrol C-5羰基被硫代羰基替换后的衍生物。接着，用计算软件ADMET Predictor软件对其进行更进一步的类药性优化。虽然只是微小的结构改造，但是新化合物的人空肠有效渗透率（PEFF）和Madin-Darby犬肾细胞（MDCK）渗透性都有很大改善。此外，monastrol C-5硫代类似物（命名Special-2）在口服给药的情况下也是安全的。
Special-2无磷脂沉积毒性，血清谷氨酸草酰乙酸转氨酶（ SGOT ）水平没有提高，并且无潜在心脏毒性。最后，分子对接方法被用来研究这些化合物的结合模式。对接研究表明，设计的化合物对于KSP具有高亲和力。因此，ADMET性质预测和分子对接方法的组合应用，可以用来提高预测的成功率并能成为设计KSP抑制剂的有效手段之一。
11.【ADME预测案例】：通过理化参数来对药物的主要清除途径进行分类
In Silico Classification of Major Clearance Pathways of Drugs with Their Physiochemical Parameters
Kusama M, Toshimoto K, Maeda K, Hirai Y, Imai S, Chiba K, Akiyama Y, Sugiyama Y. (2010) Drug Metab Dispos. 38, 1362-1370
【摘要】：药物主要清除途径的预测，对于了解其临床使用上的药代动力学特性非常重要，如药物-药物相互作用及遗传多态性等，并影响其药理/毒理作用。本研究建立了一个计算分类方法来预测药物的主要清除途径。
此方法仅需要四个理化参数：电荷、分子量（MW）、亲脂性（log D） 和血浆蛋白非结合率(fup)。无需做任何实验，只需药物的分子结构，就可计算得到这四个参数。训练集包含141已上市药物，其主要清除途径包括CYP3A4、CYP2C9和CYP2D6，OATPs肝摄取，或以原型经肾脏排泄。根据电荷进行分组后，每个药物都被绘制在由分子量、log D和fup构成的三维空间中。
然后，代表各清除途径的矩形框根据数学标准：最小体积，最大F值（正确率和召回率的调好平均数），进行绘制。通过两种方法进行验证：离一法和新数据集，发现其具有88%的预测精度。如果能够在此模型的基础上进一步修改，朝多个清除路径和/或其它途径方向发展的话。这不仅可以有助于工业科学家在药物开发的早期阶段进行决断，从而选择具有最佳药代动力学性质的候选化合物。而且对于监管机构在评估新的药物并给予合理的药代动力学监管要求的时候也是非常有用的。
12.【自建模型应用】：药物诱导磷脂质病预测模型的构建
In silico modeling to predict drug-induced phospholipidosis
Choi SS, Kim JS, Valerio Jr. LG, Sadrieh N. (2013) Toxicol. Appl. Pharmacol. 269(2):195-204
【摘要】：药物诱导的磷脂质病（DIPL）通常表现在药物开发的临床前阶段，对药物开发和安全监管审查过程具有重要影响。能够诱导产生DIPL药物的一个主要特性是具有两亲性阳离子结构。这为基于结构的解释提供了证据，并为分析具有DIPL药物的性质和结构提供了机会。
虽然，美国FDA的工作表明机器学习方法构建的DIPL定量构效关系（QSAR）预测模型可以成功应用于大多数药物；但是对一些药物仍不能给出准确预测。在本研究中，结合FDA最新的磷脂质病数据库，我们开发了一种新的算法和预测技术。经过验证，发现它是对FDA的QSAR预测模型的一个很好补充。特别是，对于具有高可信度的数据能够表现出非常好的性能。
我们建立的DIPL预测模型在严格的外部验证测试中表现出80到81%的一致性。并且，该预测模型具有高灵敏度和高特异性；在大多数情况下，这两者都高于≥80%。也就是，此模型同时具有很高的阴性和阳性预测能力。因此，此模型可用于预测新的药物是否具有诱导DIPL的性质，以进行筛选。
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大阳集团娱乐网址777技术部精取了近年来ADMET Perdictor在不同领域的应用文章，共6类。
分别为：ADMET Predictor在药化、天然药化、CADD的应用；采用ADMET Predictor预测吸收与代谢性质的应用；采用ADMET Predictor预测化合物毒性的应用；ADMET Predictor在环境、食品安全等领域的应用；ADMET Predictor在预测资源或模型开发的应用及中国用户采用ADMET Predictor发表的应用文章。
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