ADME properties

Lack of favorable ADME properties (absorption, distribution, metabolism, elimination) can preclude therapeutic use of an otherwise active molecule. The clinical pharmacokinetic parameters of clearance, half-life, volume of distribution, and bioavailability can be used to characterize ADME properties. 

To date, many of the reported ADME/Tox models have been rule based. For example, some research groups have used relatively simple filters like the rule of 5 [93] and others [94] to limit the types of molecules evaluated with in silico methods and to focus libraries for HTS. However, being designed as rapid computational alert tools aimed at a single property of interest, they cannot offer a comprehensive picture when it comes to understanding ADME properties. 

Balakin KV, Ivanenkov YA, Savchuk NP, Ivashchenko AA, Ekins S. Comprehensive computational assessment of ADME properties using mapping techniques. Curr Drug Discov Technol 2005 2 99-113. 

Hornig, M., Mamt, A. COSMOfrag a novel tool for high-throughput ADME property prediction and similarity... 

More recently, the bottleneck of drug research has shifted from hit-and-lead discovery to lead optimization, and more specifically to PK lead optimization. Some major reasons are (i) the imperative to reduce as much as feasible the extremely costly rate of attrition prevailing in preclinical and clinical phases, and (ii) more stringent concerns for safety. The testing of ADME properties is now done much earlier, i.e. before a decision is taken to evaluate a compound in the clinic. 

Because the goal of hit triage is to identify chemical series that hold promise for further optimization, an approach to characterize the ADME properties of a series, not just individual compounds is often useful. Where possible, characterizing the structure-ADME property relationship, in much the same way that a structure-potency relationship is defined, can be valuable for assessing the probability that a given structural series can be successfully optimized. The goals of this ADME property characterization are twofold (1) to identify specific structural features that may be liabilities (benefits), and (2) to identify general structure-ADME property correlations. 

Table 1 Common in vitro assays to assess ADME properties of hit compounds Clearance...

A lead is variously defined in the pharmaceutical industry as a compound derived from a hit with some degree of in vitro optimization (potency in primary assay, activity in functional and/or cellular assay), optimization of physical properties (solubility, permeability), and optimization of in vitro ADME properties (microsomal stability, CYP inhibition). Moreover, a lead must have established SAR/SPR around these parameters such that continued optimization appears possible. A lead may also have preliminary PK and in vivo animal model data. However, it is the task of the lead optimization chemist to improve PK and in vivo activity to the levels needed for identification of a clinical candidate. 

At the outset of a lead identification effort, it is imperative to establish specific criteria for potency, selectivity, ADME properties, etc. to generate a desired lead profile. This profile serves to guide the lead identification efforts based on the initial characterization of the hits. 

Criteria for biological properties may be project specific, but ADME property and physical property criteria are generally invariant. Lead profiles will be addressed in more detail in the section on parallel optimization. 

Drug candidates that are intended for oral dosing need to have good ADME properties so that they can be dosed once or twice daily. The drug should be well absorbed, survive first pass metabolism, and have sufficiently low clearance. At the lead identification stage, the primary in vitro ADME assays employed are those that assess permeability and metabolic stability. There are a variety of assays available for both parameters, as described in the previous chapter. 

As discussed above, it is important to try to optimize biological, physicochemical, and ADME properties in parallel. However, the data from all of these assays for the numerous compounds prepared by parallel synthesis make the interpretation of results challenging. The use of tools such as MVA helps in the effective utilization of all data in the optimization process. 

In a second example of the identification of IKK(3 inhibitor leads (termed IKK2 in this paper), Baxter et al. [54] report on the optimization of enzyme and cellular potency, physicochemical properties, ADME properties, and PK. This group targets... 

CXCR2 is a member of the CXC family of chemokine receptors. IL-8 activates this receptor, and an antagonist would potentially be useful for the treatment of inflammatory diseases. Baxter et al. [58] describe the parallel optimization of binding and functional potency, physicochemical properties, ADME properties, and PK. The thiol of the HTS hit was varied with typical replacements (i.e., OH, NH2, SMe, NHAc, etc.), but this only led to inactive compounds. Variation of the substituent at N(2) showed that a benzyl moiety was required (Ph, Me substituents gave inactive compounds). Variation of the C(5) substituent showed that -substituents produced optimal activity. The optimized lead has substantially improved CXCR2 binding and functional activity as well as an excellent PK profile (Scheme 13). 

DPP-4 is a serine protease that inactivates GLP-1. GLP-1 stimulates insulin secretion and suppresses glucagon release. The inhibition of DPP-4 prolongs the half-life of GLP-1 and brings about beneficial effects on glucose levels and glucose tolerance in type 2 diabetics. Backes et al. [64] report on the parallel optimization of enzyme binding affinity and inhibition, selectivity, ADME properties, and PK (Scheme 19). 

Prediction of ADME properties should be simple, since the number of descriptors underlying the properties is relatively small, compared to the number associated with effective drug-receptor binding space. In fact, prediction of ADME is difficult The current ADME experimental data reflect a multiplicity of mechanisms, making prediction uncertain. Screening systems for biological activity are typically single mechanisms, where computational models are easier to develop [1],... 

VOLSURF A Tool for Drug ADME-properties Prediction... 

In the following section, the calculation of the VolSurf parameters from GRID interaction energies will be explained and the physico-chemical relevance of these novel descriptors demonstrated by correlation with measured absorption/ distribution/metabolism/elimination (ADME) properties. The applications will be shown by correlating 3D molecular structures with Caco-2 cell permeabilities, thermodynamic solubilities and metabolic stabilities. Special emphasis will be placed on interpretation of the models by multivariate statistics, because a rational design to improve molecular properties is critically dependent on an understanding of how molecular features influence physico-chemical and ADME properties. 

In a comparison between Lipinski s rules and the J-Alert, we have found that the Rule of 5 accurately identifies some of the compounds that have unfavorable ADME properties, but also allows many of the poor compounds to go undetected. By contrast, the J-Alert identifies a much higher fraction of the unfavorable compounds but additionally identifies many ADME positives. 

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