Performing MOA Studies

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Performing MOA Studies

When performing classical steady-state mechanism of action studies, the scientist should carefully consider and incorporate the proper biochemical and statistical guidelines provided in this section. These guidelines should assist in the initial characterization of the enzyme-inhibitor complex. However, in some cases the classical steady-state experiment is not sufficient and additional characterizations are required. Examples include compounds that display tight-binding inhibition, time-dependent inhibition, covalent modification, or nonspecific inhibition of the enzyme. Therefore, we also provide guidelines to identify these additional types of inhibitors are plan the appropriate follow-up analysis.

Classical Steady-State Experiments

These types of studies involve measurements of the Vmax and KM of a substrate at a range of inhibitor concentrations. The scientist should refer to Section IVG of the QB Manual for a description of how to perform measurements of the Vmax and KM for a substrate. As mentioned previously, changes in the apparent Vmax and KM give the scientist a view of the binding modality (competitive, noncompetitive, or uncompetitive) and the potency (KI and KI’). Figure 6 illustrates the classical steady-state experiment used to determine the binding modality and potency.

The methodology proposed here to determine the binding potency and modality of an inhibitor is derived from a steady-state model of enzyme kinetics. The term steady-state refers to a constant concentration of the enzyme-substrate complex present during the reaction. As summarized by Copeland (Enzymes 2ed) and Segal (Enzyme Kinetics), there are several assumptions that simplify the mathematical treatment of the kinetics. When these assumptions fail, the steady-state MoA model proposed here is not valid.

1. The enzyme is acting catalytically and the concentration of substrate is much greater than the concentration of enzyme.
2. During the initial phase of the reaction (initial velocity), there is no buildup of any intermediate other than the enzyme-substrate complex.
3. There is very little product formed over the course of the reaction so that the depletion of substrate is minimal and the reverse reaction is insignificant.
4. The concentration of inhibitor is much greater than the concentration of enzyme so that the depletion of free inhibitor resulting from the formation of the enzyme-inhibitor complex is minimal.

The scientist should utilize the following guidelines in the design, execution, and analysis of a classical MoA experiment.

Guidelines for Assay Design

• As described in Section IVD, it is essential to ensure that the enzyme, substrate, co-factors, and buffer conditions have been fully evaluated and characterized. Wherever possible, the scientist should strive to achieve in-vitro conditions that will best represent the physiological conditions in a robust, reproducible manner. The selection of these factors can have a large impact on the binding modality and potency observed.
• An enzyme titration should be performed to determine the concentration of active sites in the assay. Consult Copeland, Enzyme 2ed, pg313 or an experienced enzymologist for more information.
• There should be at least 5 concentrations of substrate tested, spanning a range of at least ½xKM to 5xKM, for each concentration of inhibitor tested. As illustrated in Figure 7, the ability to distinguish a competitive inhibitor from a noncompetitive or uncompetitive inhibitor is increasingly enhanced at concentrations of substrate above its KM value. The ability to distinguish noncompetitive inhibition from uncompetitive inhibition is more challenging and can be improved with very accurate determinations of the apparent KM. Therefore, the scientist should strive to judiciously increase the range and number of concentrations of substrate tested.
  Figure 7 – Residual plots demonstrating the difference in observed rate of enzyme activity (z-axis) at each concentration of substrate (y-axis) and inhibitor (x-axis) for 2 binding modalities. (a) Competitive Inhibition vs Noncompetitive inhibition. (b) Competitive inhibition vs Uncompetitive inhibition. (c) Noncompetitive vs Uncompetitive inhibition. Taken together, competitive inhibitors are best distinguished from noncompetitive and uncompetitive inhibitors at both high [substrate] and high [inhibitor]. Noncompetitive and uncompetitive inhibitors are best distinguished from each other at [substrate] and [inhibitor] near their binding constants (KM and KI). Therefore, the range and density of concentrations tested are both important.
• The plot of the [substrate] vs initial velocity should not display sigmoidal kinetics, unless it is a mechanistic feature of substrate binding and catalysis for that enzyme. The impact of sigmoidal kinetics on the KM curve is illustrated in Figure 8. Sigmoidal kinetics may be a sign of an impure enzyme or the presence of multiple isoforms of the enzyme (ex. multiple phosphorylation states of the same kinase). Refer to Copeland, Enzyme 2ed, pg382 or an enzymologist experienced with sigmoidal kinetics.
  Figure 8 – Comparison on enzyme data for a system with a proper slope of 1 and another displaying a sigmoidal relationship (ex. slope of 2) between the substrate concentration tested and the rate observed.
• The initial velocity should be measured. In order for the steady-state assumptions to hold, it is recommended that less than 10% of the substrate be converted to product. Section IVC describes this guideline in more detail. However, initial velocity conditions do not infer linearity and the user should refer to the guideline directly below.
• The formation of product should be linear with respect to time. This is best achieved by measuring the rate of product formation at the chosen concentrations of substrate using the assay conditions, detection system, and instruments that will be used for the final assay. Linearity should be assessed visually from plots of the raw data.
• There should be at least 8 concentrations of inhibitor tested at each concentration of substrate. The range of inhibitor concentrations tested should span the KI or KI’, depending on the binding modality. Reporting of binding constants outside of the range of concentrations tested should be avoided. It is also recommended to include inhibitor concentrations at or above ~10xKI to ensure maximum inhibition and the identification of any potential Partial Inhibitors. It should be noted that any observation of Partial inhibition could instead be a consequence of a compound’s poor solubility.
• Where available, a control inhibitor should be evaluated under the exact conditions that will be used for the final assay.
• In addition to the experimental wells containing a matrix of substrate and inhibitor dilutions, the final assay should include both high and low controls. The high control should contain the substrate titration without inhibitor to reflect the maximum enzyme activity at each substrate concentration. The low control should contain the substrate titration without enzyme or substrate and without inhibitor. The low controls should reflect the signal expected for no enzyme activity at each substrate concentration. Depending on the composition of the inhibitor stocks, DMSO might be needed in the control wells to assure consistency across all the experiments.
• The concentration of DMSO should be kept constant in MoA experiments for a particular target. DMSO can have a significant impact on enzyme activity and the concentration of DMSO in the wells containing compound should be identical to the concentration of DMSO in the control wells (described directly above). DMSO can also impact the solubility of a compound and its observed potency. Therefore, the concentration of DMSO should be consistent in replicate MoA experiments (or in comparison to IC50 experiments).
• It is recommended to evaluate, in the standard assay conditions, dependence of [enzyme] on the IC50 of the compounds to be tested. Shifts in the IC50 as a function of the [enzyme] is an indication of tight-binding inhibition and/or solubility issues. When this is encountered, the scientist should consult with an enzymologist experienced with tight-binding inhibition.
• If detergents are required for enzyme activity or automation, the scientist should strive to maintain their concentrations well below the critical micelle concentration (CMC). The formation of micelles, at high concentrations of detergents, can interfere with the determinations of the binding modality and potency. An exception to this rule would include assays requiring detergents as part of the mechanistic evaluation. If the assay can only be run above the CMC, the scientist should consult with an enzymologist experienced with lipids, micelles, and surface dilution kinetics.
• The reaction should be measured under steady-state conditions. As described by Copeland in Enzymes 2ed, this includes the following … 1) there should not be any appreciable buildup of any enzyme intermediates other than the ES complex, 2) the [substrate] should be >> [enzyme], and 3) the initial phase of the reaction is measured so that the [product] ~ 0, the depletion of substrate is minimal, and the reverse reaction is insignificant.
• The concentration of a required cofactor should be >> [enzyme].

Statistical Validation of the Designed Assay

The requirements for statistical validation of an MoA assay can be divided into two situations: (1) high-throughput assays using automation that can test many compounds, and (2) low-throughput assays in which only one or a few compounds are tested. In the first case, a replicate-experiment study should be performed as described in Section III of the QB manual. Briefly, 20-30 compounds should be tested in two independent runs. Then the MSD or MSR and limits of agreement are determined for each of the key results, including Vmax, Km, Ki, Ki’, and α or αinv. Specific acceptance criteria have not been determined. The reproducibility should be judged as suitable or not for each situation. For low-throughput assays, a replicate-experiment study is not required. At a minimum, key results from the MoA experiment, such as Vmax, Km, and Ki, should be compared to previous/preliminary experiments to ensure consistency. The data from the MoA experiment should be examined graphically for outliers, goodness of fit of the model to the data, and consistency with the assumptions and guidelines for designing and running the assay (see Guidelines for Assay Design above and Guidelines for Running the Assay below).

Guidelines for Running the Assay

• The assay should be run under the exact same conditions as developed using the guidelines above. In addition, the assay should be run within the timeframe where the reagents are known to be stable.
• When a control inhibitor is included, then the KI (and/or KI’) value should be compared with legacy data to ensure robust, quality results. It is also recommended to include additional inhibitors with alternative binding modalities, if available.
• The KM and Vmax values from the high controls and the signal from the low controls should be compared with the legacy values determined in identical conditions, as described above.
• A standard curve should be included for detection systems yielding signals that are nonlinear with respect to the amount of product formed. This nonlinearity is a common feature in fluorescent-based assays. The standard curve should be used to covert the signal produced to the amount of product formed. The resulting amount of product formed over the course of the assay time should be used in the data fitting methodologies. Please refer to the QB Manuel titled ‘Immunoassay Methods’.

Guidelines for Data Fitting and Interpretation

• The multivariate dataset (v, [I],[S]) should be fit using a non-linear regression analysis with the appropriate models described below. Linear transformations of the data should be avoided as they will distort the error of the experiment and were historically used only before the introduction of computer algorithms.
• The scientist should perform any necessary background corrections, before the multivariant fitting, so that a signal or rate of 0 represents that expected for conditions lacking enzyme activity. Depending on the assay design, this may include a single background correction applied to the entire experiment or several different corrections. The latter should be used when the background signal varies with the [substrate] tested. Here there should be a background correction for each [substrate] tested.
• The traditional model of general mixed inhibition is (P1)

  where v is the speed of the reaction (slope of product formed vs. time), Vmax is the upper asymptote, [S] is the substrate concentration, and [I] is the inhibitor concentration. See the glossary for definitions of Km, Ki, and Ki’. This model can also be written as

  
  

  (P2)

  
  

  (P3)

  where α = 1/αinv = Ki’/Ki. This model reduces to specific models for competitive, non-competitive, and un-competitive inhibition as described in this table:

  The inhibitor binds only to the enzyme-substrate complex at a location outside the active site. infinite

  Inhibition	Description	Ki	Ki’	Ki’/Ki
  Competitive	The inhibitor binds only to free enzyme. This binding most often occurs in the active site at the precise location where substrate or cofactor (being evaluated in the MoA study) also binds.	finite	Infinite	infinite
  Mixed	These inhibitors display properties of both competitive and noncompetitive inhibition.	finite	Finite	> 1
  Noncompetitive	The inhibitor binds equally well to both free enzyme and the enzyme-substrate complex. Consequently, these binding events occur outside the active site.	finite	Finite	= 1
  Uncompetitive	The inhibitor binds only to the enzyme-substrate complex at a location outside the active site.	infinite	Finite	= 0

  Another form of this model that has better statistical properties, in terms of parameter estimation and error determination, is:

  
  

  (P4)

  where

  

  
  

  

  
  

  More details on these models can be found in a paper written by the primary authors of this chapter (to be submitted to JBS).
• The following data analysis steps could be followed:
  1. Fit a robust multiple linear regression of 1/v vs. 1/[S], [I]/[S], and [I]. This provides starting values of the θ parameters for the non-linear regression in the next step.
  2. Fit model P4 to the data (v, [I], [S]).
  3. Calculate the parameters of interest from the θ values.
  4. Calculate confidence limits for each key parameter value using Monte Carlo simulation.
  5. Make decisions of mechanism based on the value of α or αinv and the associated confidence limits.
• Alpha or alpha inverse should be used to assign the binding modality. If alpha is less than 1, the mechanism is:
  1. Uncompetitive if the upper confidence limit of alpha is less than 0.25
  2. Noncompetitive if the lower confidence limit of alpha is more than 0.25
  3. Not competitive, otherwise

     
     

     if alpha inverse is less than 1, then the mechanism is:
  4. Competitive if the upper confidence limit of alpha is less than 0.1
  5. Noncompetitive if the lower confidence limit of alpha is more than 0.5
  6. Mixed, if both confidence limits are within [0.1, 0.5]
  7. Not declarable, otherwise

     
     

     The details of how these cutoffs were chosen are in the JBS paper.
• When the signal measured at 10xKI (representing full enzyme inhibition by the compound) is >>0 (baseline corrected), the compound is displaying Partial (and/or Allosteric) Inhibition. This difference might also be observed when the incorrect conditions were chosen for the low control to represent no enzyme activity, if there was not enough inhibitor (relative to the KI or KI’) to achieve maximum inhibition, and/or if the compound tested is poorly soluble.
• When the KI or KI’ resulting from the fit is within 10-fold of the concentration of active sites in the assay, the compound will start to display tight-binding inhibition. Inaccuracies in the binding modality and potency will result. In some cases where the inhibitor is not soluble, tight binding inhibition may exist at much higher KI or KI’ values. As recommended previously, the dependency of the enzyme concentration on the inhibitor’s potency is the best method to identify tight-binding inhibition. The scientist should consult with an expert in tight-binding inhibition to further characterize the inhibitor.
• Data suggesting that a compound is noncompetitive (and in some cases mixed) should be handled with caution. Compounds that are time-dependent, irreversible, poorly soluble, nonspecific, and/or tight-binding will display a noncompetitive/mixed phenotype in this type of classical steady-state experiment. As such, it is critical to evaluate these additional potential mechanisms of action, described herein.
• Additional recommendations for data analysis can be found in the next section.

When the Steady-State Assumptions Fail

The steady-state MoA model proposed for here for data fitting requires several important assumptions hold true. While a majority of these assumptions are covered in the previous sections, the invalidation of a few key assumptions should prompt the scientist to perform additional mechanistic characterizations. These key assumptions, a mechanism to flag their breakdown in the steady-state MoA model, and a recommended plan of action are presented.

Tight Binding Inhibition

The [inhibitor] in solution should be much greater than the [enzyme] in the assay. This assumption fails most frequently in 2 circumstances. First, some compounds bind to their target with such high affinity (appKI values within 10 fold of the [enzyme]) that the population of free inhibitor molecules is significantly depleted by formation of the EI complex. Second, some compounds are both very potent and poorly soluble. The poor solubility of the inhibitor will increase the observed appKI value (relative to the [enzyme]). In both cases, the compounds are called tight binding inhibitors.

• How can tight binding inhibitors be flagged in the steady-state MoA model?
  • Regardless of their true binding modality, they display a noncompetitive phenotype.
  • They have observed appKI values between ½ and 10-fold of the [enzyme] in the assay.
  • Poorly soluble compounds may also display tight binding inhibition. This is often masked by an inflated observed appKI value.
• What is the recommended plan for an appropriate characterization?
  • Calculate the dependence of the IC50 values on the [enzyme]. Using a fixed concentration of substrate at KM, the IC50 of the inhibitor should be measured at =5 concentrations of enzyme. If the IC50 changes significantly as a function of the [enzyme], the inhibitor is displaying tight binding properties and requires further characterization. If the IC50 does not change significantly, the compound is not tight binding, this key assumption ([Inhibitor]>>[Enzyme]) is true, and the steady-state MoA model is valid. These 2 phenotypes are illustrated in Figure 9.
    Figure 9 – Plotting the IC50 vs [Enzyme] will reveal whether a compound is tight binding. As depicted on the left, no change in the IC50 suggests that the compound is not tight binding and the assumption ([I] >> [E]) holds true. As depicted on the right, a change in the IC50 (with a slope of 0.5) suggests that the compound is tight binding and requires additional characterization.
  • Calculate the dependence of the IC50 values on the [substrate]. Using a fixed concentration of enzyme, the IC50 of the inhibitor should be measured at >5 concentrations of substrate. The range of concentrations of substrate should span the KM (as recommended previously). As illustrated in Figure 10, the change in the IC50 vs [substrate] is described by the equation listed below and yields the true binding potency (KI and KI’). The ratio of KI’/KI (termed alpha, α) reflects the binding modality. Inhibitors with alpha values statistically equal to 1.0 are noncompetitive, values statistically less than 1.0 are uncompetitive, and values statistically greater than 1.0 are competitive.
    Figure 10 – A plot of the IC50 vs [substrate] will reveal the binding modality for a tight binding inhibitor. The quality of this assessment is predicated on the choice of a range of substrate concentrations that span the KM. The graph illustrates that competitive inhibition is best identified at substrate concentrations above KM. In contrast, uncompetitive inhibition is best identified at substrate concentrations below KM. The true KI and/or KI’ values can be obtained from a fit using the model below.

    
    

    
  • These methodologies are described in more detail in Chapter 9 of Enzymes 2ed by Copeland. We also recommend consulting with a statistician and an enzymologist experienced with tight binding inhibition.

Time-Dependent Inhibition

When the reaction is started with enzyme, there should be a linear relationship between the enzyme reaction time and the amount of the product formed from that reaction. This linearity should be preserved for all enzyme reactions lacking inhibitor or having rapid equilibrium binding events outside of the time window measured. However, the addition of inhibitor may result in a nonlinear progress curve (Figure 11) with an initial burst of enzyme activity (vi) followed by a final, slower steady-state rate (vs). Although the steady-state MoA model may still apply under some circumstances, additional characterizations are required.

• How can time dependent inhibitors be flagged in the steady-state MoA model?
  • For kinetic enzyme assays, the progress curve showing product formation over time is nonlinear (Figure 11).
  • For endpoint enzyme assays, time dependent inhibitors can display a noncompetitive phenotype regardless of their true binding modality. Otherwise, they can be identified by observing a shift in inhibitor potency with either a change in the enzyme reaction time and/or a change in the enzyme/inhibitor pre-incubation time.
• What is the recommended plan for an appropriate characterization?
  • More appropriately characterize and model the nonlinear progress curves (product formed vs time) observed. Illustrations of these progress curves and the appropriate models to use are found below in Figure 11. The resulting fit of the data to the nonlinear model should produce the vi, vs, and kobs for all the [substrate] and [inhibitor] tested.
    Figure 11 – Progress curves for linear, rapid equilibrium inhibition (left) and nonlinear, time dependent inhibition (right). Nonlinear progress curves resulting from time dependent inhibition can be fit to the model shown above. The resulting fit will yield the initial velocity (vi), steady-state velocity (vs), and the rate constant for the interconversion between vi and vs (kobs), under the conditions tested. These values can be used to assess the true binding potency and modality.
    During this evaluation, kobs values reflecting timepoints (t) outside of the window tested should be avoided. For example, valid kobs values from a kinetic run starting at 2min and ending at 60min should range between 0.5min-1 to 0.08min-1. As a general rule, the total time of the reaction should be 5 times greater than 1/kobs. As a result, the scientist may need to choose a smaller range of [substrate] spanning KM and [inhibitor] spanning appKI.
  • In most cases, the initial (vi) and steady-state (vs) velocities can be fit separately to the steady-state MoA model (presented in the previous section) to yield the binding potency (KI and/or KI’ value) and modality for each phase of inhibition.
  • A more traditional approach to determine the apparent potency of the inhibitor requires the scientist to plot the kobs values as a function of the [inhibitor] at a fixed [substrate]. This can yield 2 main types of plots illustrated in Figure 12 below. 1) If there is a linear relationship between the kobs and the [inhibitor] tested, the one-step model shown should be used to determine the appKI (potency at the steady-state velocity, vs). 2) If there is a hyperbolic relationship between the kobs and the [inhibitor] tested, the two-step model shown should be used to determine the appKI (potency at the initial velocity, vi) and the appKI* (potency at the steady-state velocity, vs).
    Figure 12 – A plot of the kobs vs [inhibitor] will allow for the determination of the appKI value for a time dependent inhibitor. If the relationship between kobs and the [inhibitor] is linear, the one-step model shown above should be used. If the relationship is nonlinear, the two-step model should be used.
  • A more traditional approach to determine the binding modality of a time dependent inhibitor requires a determination of the appKI, from the previous kobs vs [inhibitor] plot, at each [substrate] spanning the KM. The appKI (and appKI*) can then be graphed as a function of [substrate] and fit to the model shown below (Figure 13). The ratio of KI’/KI (termed alpha, a) determined from the model below will reflect the binding modality. Inhibitors with alpha values statistically equal to 1.0 are noncompetitive, values statistically less than 1.0 are uncompetitive, and values statistically greater than 1.0 are competitive.
    Figure 13 – A plot of the appKI (and appKI*) vs [substrate] will allow for the determination of the true binding potency and modality. The modeled lines above are generated using the equation shown directly below where alpha = KI’/KI.

    Model to Determine Time Dependent MoA

    

    
    

    Where possible, we recommend avoiding the iterative fitting into the one-step or two-step models and the model directly above. The scientist should consult with a statistician and enzymologist to perform a global fit of the data to an equation where the one-step or two-step models are solved for the appKI shown directly above.
  • A parallel approach to determine the binding modality requires the scientist evaluate the kobs values as a function of the [substrate] at a fixed [inhibitor]. The kobs of a competitive inhibitor will decrease with increasing [substrate] relative to KM. The kobs of an uncompetitive inhibitor will increase with increasing [substrate] relative to KM. The kobs of a noncompetitive inhibitor will not change with increasing [substrate] relative to KM). These trends are illustrated in Figure 14.
    Figure 14 – A plot of the kobs vs [substrate] will reveal the binding modality for a time dependent inhibitor. It is important to choose [substrate] well above and below the KM to improve the ability to best distinguish the true binding modality.
  • These methodologies are described in more detail in Chapter 10 of Enzymes 2ed by Copeland. Also be aware that a compound can display both time dependent and tight binding properties. This would require a combination of experiments described above that may require the assistance of a statistician or an experienced enzymologist.

Covalent Modification

During the initial phase of the reaction (initial velocity), there is no buildup of any intermediate other than the enzyme-substrate complex. This assumption most often fails when a compound is an irreversible inhibitor of the enzyme. This type of inhibition can be the result of an immeasurably slow koff value and/or covalent modification of the enzyme.

• How can irreversible inhibitors be flagged in the steady-state MoA model?
  • Regardless of their true binding modality, they display a noncompetitive phenotype.
  • Irreversible inhibitors are time dependent with vs values that approach zero. In contrast, reversible time dependent inhibitors have finite, non-zero vs values. The quality of this observation can be limited by the timepoints measured and the [inhibitor] evaluated.
  • The observed koff value is zero. This can be observed in a plot of the kobs as a function of the [inhibitor], shown in Figure 12. Irreversible inhibitors will yield a y-int (koff) of zero.
• What is the recommended plan for an appropriate characterization?
  • In addition to the characterizations described in the sections above, the scientist can measure the release of inhibitor from the enzyme-inhibitor complex. This is often performed by pre-incubating the enzyme with inhibitor at 10xKI to achieve 100% inhibition (all enzyme is in the EI complex reflecting vs), then diluting the assay 30 fold with substrate, and continuously (kinetically) measuring product formation. As illustrated in Figure 15, reversible inhibitors will regain enzyme activity while irreversible inhibitors remain inactive. This experiment can be properly interpreted when 3 controls are included containing 1) no inhibitor throughout to reflect full enzyme activity at the amount of DMSO tested, 2) 10xKI throughout to achieve 100% inhibition, and 3) 0.3xKI throughout to reflect the expected amount of inhibition remaining after substrate dilution. Assuming the 10xKI control is inactive, the final rate (vs) for the experiment can be divided by the final rate of the 0.3xKI control to yield the fraction of recovered activity.
    Figure 15 – The recovery of enzyme activity following dilution of the EI complex can be an indication of the reversibility of the inhibitor. Irreversible inhibitors (right) will not recover any enzyme activity following dilution of the EI complex with substrate. In contrast, a reversible inhibitor (left) will recover enzyme activity equivalent to the 0.3xKI control.
  • It is important to remember the there is no clear distinction between reversible and irreversible time dependent inhibition. The quality of the determination can often reflect the range and density of timepoints measured, [inhibitor] chosen, and other limitations specific to the assay. Therefore, it would be wise for the scientist to consult an analytical chemist to perform a MS-based strategy to confirm irreversible inhibition resulting from covalent modification of the enzyme.

Nonspecific Inhibition

Some compounds may form large colloid-like aggregates that inhibit activity by sequestering the enzyme. These types of compounds can display enzyme dependency, time-dependent inhibition, poor selectivity against unrelated enzymes, and binding modalities that are not competitive. This can be especially problematic when an enzyme is screened against a large diversity of compounds in a screening campaign. Although these compounds do not formally violate the steady-state assumptions, they can generate misleading results which produce inaccurate characterizations of the inhibitor-enzyme complex. The scientist is encouraged to read the Shoichet Review published in Drug Discovery Today (2006). The chart below was taken from that reference and provides an introduction to the considerations that should be made for evaluating these types of inhibitors. As well the NCGC has profiled the MLSMR present in PubChem to identify compound that show “aggregation-based” inhibition (Feng et al., 2007).