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Relationship: 1833


The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Binding of MSAs to microtubules leads to Disturbance in microtubule dynamic instability

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Microtubule interacting drugs lead to peripheral neuropathy adjacent Not Specified Not Specified Arthur Author (send email) Under development: Not open for comment. Do not cite

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help
Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

It is well known that the binding of taxol and MSAs like epothilones and discodermolide to microtubules stabilizes microtubules thereby promoting polymerization and concomitantly suppressing depolymerisation. Therefore, they directly disturb microtubule dynamic instability. [1-9]

It is assumed that the M-Loop, which is part of the taxane pocket, undergoes conformational changes and gets more structured as a short helix is formed upon MSA binding. This structuring promotes the assembly and stabilization of microtubules as it is needed for lateral tubulin interactions. [10, 11]

Mutations in the β-tubulin gene were identified in patients with taxol-resistant non-small-cell lung cancer. Patients with β-tubulin mutations did not respond to taxol-treatment, whereas patients without β-tubulin mutations had complete or partial responses and survived longer. β-tubulin mutations were therefore identified as predictor of taxol-response thereby confirming β-tubulin as the binding and interaction site of taxol. [12]

In two taxol-resistant ovarian cancer cell lines, two point mutations were identified in the β-tubulin gene. Taxol-driven polymerization was shown to be impaired in these cells. Taxol-resistant cells did not exhibit microtubule polymerization upon taxoll treatment whereas the parental cells show increasing tubulin-polymerization with increasing doses of taxol. [13]

In two epothilone-resistant ovarian carcinoma cell lines two point mutations were identified in the β-tubulin gene. Epothilone- as well as taxol-driven polymerization was shown to be impaired in these cells while parental cells exhibit dose-dependent increase in tubulin polymerization upon epothilone A- and taxol-treatment. [14]

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

The binding of MSAs to microtubules is extensively studies and well established. Its impact of this interaction on microtubule dynamic instability is addressed in numerous studies and the findings are largely consistent in the point of stabilization of microtubules accompanied by the disturbance of microtubule dynamic instability.

It has to be noted that microtubule destabilizing agents like the vinca alkaloids are known to bind to microtubules and disturb microtubule dynamic instability as well. However, vinca alkaloids differ in their mode of action as they bind to the end of microtubules and, in case of stoichiometric binding, promote depolymerization. [19]

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help
Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help
Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help


List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

1. Schiff, P.B., J. Fant, and S.B. Horwitz, Promotion of microtubule assembly in vitro by taxol. Nature, 1979. 277(5698): p. 665-667.

2. Bollag, D.M., et al., Epothilones, a New Class of Microtubule-stabilizing Agents with a Taxol-like Mechanism of Action. Cancer Research, 1995. 55(11): p. 2325-2333.

3. Hung, D.T., J. Chen, and S.L. Schreiber, (+)-Discodermolide binds to microtubules in stoichiometric ratio to tubulin dimers, blocks taxol binding and results in mitotic arrest. Chemistry & Biology, 1996. 3(4): p. 287-293.

4. Kowalski, R.J., et al., The Microtubule-Stabilizing Agent Discodermolide Competitively Inhibits the Binding of Paclitaxel (Taxol) to Tubulin Polymers, Enhances Tubulin Nucleation Reactions More Potently than Paclitaxel, and Inhibits the Growth of Paclitaxel-Resistant Cells. Molecular Pharmacology, 1997. 52(4): p. 613-622.

5. ter Haar, E., et al., Discodermolide, A Cytotoxic Marine Agent That Stabilizes Microtubules More Potently Than Taxol. Biochemistry, 1996. 35(1): p. 243-250.

6. Derry, W.B., L. Wilson, and M.A. Jordan, Substoichiometric Binding of Taxol Suppresses Microtubule Dynamics. Biochemistry, 1995. 34(7): p. 2203-2211.

7. Dumontet, C. and M.A. Jordan, Microtubule-binding agents: a dynamic field of cancer therapeutics. Nature Reviews. Drug Discovery, 2010. 9(10): p. 790-803.

8. Jordan, M.A. and L. Wilson, Microtubules as a target for anticancer drugs. Nature Reviews Cancer, 2004. 4: p. 253.

9. Carozzi, V.A., A. Canta, and A. Chiorazzi, Chemotherapy-induced peripheral neuropathy: What do we know about mechanisms? Neurosci Lett, 2015. 596: p. 90-107.

10. Prota, A.E., et al., Molecular mechanism of action of microtubule-stabilizing anticancer agents. Science, 2013. 339(6119): p. 587-590.

11. Snyder, J.P., et al., The binding conformation of Taxol in β-tubulin: A model based on electron crystallographic density. Proceedings of the National Academy of Sciences, 2001. 98(9): p. 5312-5316.

12. Monzó, M., et al., Paclitaxel Resistance in Non–Small-Cell Lung Cancer Associated With Beta-Tubulin Gene Mutations. Journal of Clinical Oncology, 1999. 17(6): p. 1786-1786.

13. Giannakakou, P., et al., Paclitaxel-resistant Human Ovarian Cancer Cells Have Mutant β-Tubulins That Exhibit Impaired Paclitaxel-driven Polymerization. Journal of Biological Chemistry, 1997. 272(27): p. 17118-17125.

14. Giannakakou, P., et al., A common pharmacophore for epothilone and taxanes: Molecular basis for drug resistance conferred by tubulin mutations in human cancer cells. Proceedings of the National Academy of Sciences, 2000. 97(6): p. 2904-2909.

15. Witte, H., D. Neukirchen, and F. Bradke, Microtubule stabilization specifies initial neuronal polarization. The Journal of Cell Biology, 2008. 180(3): p. 619-632.

16. Jordan, M.A., et al., Mechanism of mitotic block and inhibition of cell proliferation by taxol at low concentrations. Proceedings of the 

National Academy of Sciences of the United States of America, 1993. 90(20): p. 9552-9556.

17. Kowalski, R.J., P. Giannakakou, and E. Hamel, Activities of the Microtubule-stabilizing Agents Epothilones A and B with Purified Tubulin and in Cells Resistant to Paclitaxel (Taxol®). Journal of Biological Chemistry, 1997. 272(4): p. 2534-2541.

18. Risinger, A.L. and S.L. Mooberry, Cellular studies reveal mechanistic differences between taccalonolide A and paclitaxel. Cell Cycle, 2011. 10(13): p. 2162-2171.

19. Dumontet, C. and B.I. Sikic, Mechanisms of Action of and Resistance to Antitubulin Agents: Microtubule Dynamics, Drug Transport, and Cell Death. Journal of Clinical Oncology, 1999. 17(3): p. 1061-1061.