Microsomal Stability Assay

Understand the metabolism of your compounds by using our microsomal stability assay to measure in vitro intrinsic clearance or to identify metabolites formed.

Microsomal stability is one of Cyprotex's in vitro ADME screening services. Cyprotex delivers consistent, high quality data with cost-efficiency that comes from a highly automated approach.

Measurement of in vitro intrinsic clearance using liver microsomes:

• The liver is the most important site of drug metabolism in the body. Approximately 60 % of marketed compounds are cleared by hepatic cytochrome P450 (CYP)-mediated metabolism¹
• Liver microsomes are subcellular fractions which contain membrane bound drug metabolizing enzymes such as CYP
• Microsomes can be used to determine the in vitro intrinsic clearance of a compound
• The use of species-specific microsomes can be used to enable an understanding of interspecies differences in drug metabolism
• Microsomes are easy to prepare, use and store enabling cost efficiencies over whole cell models
• Microsomes are pooled from multiple donors to minimize the effect of interindividual variability
• Microsomes are fully characterized using probe substrates to ensure activity is maintained between batches

Follow on metabolite profiling and structural elucidation:

Cyprotex's microsomal stability assay can be extended to profile the metabolites that are formed. Cyprotex’s biotransformation services are supported by high-resolution, accurate mass spectrometry. These services can provide information on an individual species’ metabolite profile, or a cross-species comparison to identify potential differences in metabolism which could in turn help to interpret pharmacology and toxicity data. Structural elucidation can also be performed on the potential metabolites’ MS/MS fragmentation data. All biotransformation studies are performed by a dedicated team of experts.

Please refer to our Metabolite Profiling and Identification section for further details.

The liver is the main organ of drug metabolism in the body. Subcellular fractions such as liver microsomes are useful in vitro models of hepatic clearance as they contain many of the drug metabolizing enzymes found in the liver. Microsomes are easy to prepare and can be stored for long periods of time. They are easily adaptable to high-throughput screens which enable large numbers of compounds to be screened rapidly and inexpensively.

Liver microsomes are incubated with the test compound at 37°C in the presence of the co-factor, NADPH, which initiates the reaction. Samples are removed at the appropriate time points and the reaction is terminated by the addition of acetonitrile. Following centrifugation, internal standard is added to the supernatant which is analyzed by LC-TOF-MS. The disappearance of test compound is monitored over a 45 minute time period. An example of a typical depletion profile is shown in Figure 3.

The natural log of the percent remaining is plotted against time and the gradient of the line determined, where percent remaining is the compound ratio (compound peak area divided by internal standard peak area) as a percentage of the 0 minute time point.

The following equations are used when analyzing data from this assay:

The data can be used in several ways:

The compounds can be ranked in terms of their intrinsic clearance values. Unless the compound is a pro-drug, very highly cleared compounds are generally considered to be unfavorable as they are likely to be rapidly cleared in vivo resulting in a short duration of action. Classification bands can be used to categorize compounds into low, medium or high clearance. The CL_int classification bands for each species in Table 1 are calculated from a rearrangement of the well stirred model⁵ detailed in the following equation assuming an extraction ratio (E) of 0.3 and 0.7 for the low and high boundaries, respectively. This can then be scaled to intrinsic clearance (mL/min/kg) using the relevant liver weights³ and microsomal protein concentration^6,7 obtained from the literature. Due to lack of literature information, monkey and mouse microsomal protein concentration values of 32 and 45 mg microsomal protein/g liver were assumed, to match human and rat respectively. For simplicity, fu has been assumed to be 1, this represents the worst case scenario and where compounds are more highly plasma protein bound less stringent criteria would apply.

where 

_{CLH = E x QH}

_{QH = liver blood flow (mL/min/kg)⁵}

_{E = extraction ratio}

_{CLH = hepatic clearance (mL/min/kg)}

_{fu = fraction unbound in plasma (assumed at 1)}

The data can be used in conjunction with other in vitro parameters to predict the pharmacokinetics of a compound in vivo (available through Cyprotex’s PK prediction service).

Species specific differences in drug metabolism can be investigated. This may be useful in identifying an appropriate species for preclinical development.

There are several reasons why hepatic microsomal CL_int data may under predict in vivo clearance:

1. The important metabolic fates for the compound are not present or activated in microsomes supplemented with NADPH e.g. UGT conjugation or cytosolic aldehyde oxidation.

2. Other tissues may also contribute to test compound metabolism e.g. intestine or kidney.

3. Other clearance routes, e.g. renal excretion of test compound, are important in the total drug clearance. In addition, several laboratories have noted there is a systematic under-prediction of in vivo clearance from human liver microsome stability data even for drugs cleared predominantly by CYP mediated hepatic metabolism. Causes of the under-prediction have not been defined and are potentially multifactorial, in light of this several laboratories have proposed empirical approaches to correct the observed bias and improve the accuracy of clearance predictions^8,9.

In addition, several laboratories have noted there is a systematic under-prediction of in vivo clearance from human liver microsome stability data even for drugs cleared predominantly by CYP mediated hepatic metabolism. Causes of the under-prediction have not been defined and are potentially multifactorial, in light of this several laboratories have proposed empirical approaches to correct the observed bias and improve the accuracy of clearance predictions^8,9.

Microsomes are adaptable to high-throughput screening and enable large numbers of compounds to be screened inexpensively. Our clients tend to use this assay as an initial screen to rank order compounds of interest in terms of their metabolic stability, and then perform a secondary screen on a smaller number of selected compounds using hepatocytes.

A minimum of two positive control compounds are included for each species and is dependent on the type of protein and cofactor used.

A control incubation is performed in the absence of cofactor to reveal any chemical instability or non-NADPH dependent enzymatic degradation.

A control is included that contains all reaction components with the exception of the test compound. This control identifies any potential interfering component which may affect the analysis.

The microsomal stability assay is primarily used to investigate Phase I metabolism using NADPH as the enzyme co-factor. However liver microsomes can also be used to study Phase II metabolism if the correct incubation conditions are used. We have recently validated this using the pore forming agent alamethicin in conjunction with appropriate Phase II cofactors for example UDPGA. This allows you to gain an understanding as to the contribution Phase II metabolism has on the overall metabolism of a test compound. It is also possible to study coupled Phase I and Phase II metabolism by using all of the relevant co-factors in the incubation.

_{1) LeCluyse EL and Alexandre E (2010) Isolation and culture of primary hepatocytes from resected human liver tissue Methods Mol Biol 640; 57-82
2) Davies B. and Morris T. (1993) Physiological parameters in laboratory animals and humans Pharma Res 10(7); 1093-1095
3) Riley RJ et al. (2005) A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes Drug Metab Dispos 33; 1304-1311
4) Wood FL et al. (2005) Clearance prediction methodology needs fundamental improvement: trends common to rat and human hepatocytes/microsomes and implications for experimental methodology Drug Metab Dispos 45(11); 1178-1188
5) Houston JB (1994) Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance Biochem Pharmacol 47(9); 1469-1479
6) Barter ZE et al. (2007) Curr Drug Metab 8(1); 33-45
7) Sohlenius-Sternbeck AK et al. (2010) Evaluation of the human prediction of clearance from hepatocyte and microsome intrinsic clearance for 52 drug compounds Xenobiotica 40(9); 637-649
8) Iwatsubo T et al. (1997) Prediction of species differences (rats, dogs, humans) in the in vivo metabolic clearance of YM796 by the liver from in vitro data JPET 283(2); 462-469
9) Poulin P et al. (2012) In vitro–in vivo extrapolation of clearance: modeling hepatic metabolic clearance of highly bound drugs and comparative assessment with existing calculation methods J Pharm Sci 101(2); 838-851}