Methods and apparatuses for prediction of mechanism of activity of compounds

Patent metadata

Field	Value
Publication number	US-12411128-B2
Application number	US-201816019332-A
Country	US
Kind code	B2
Filing date	Jun 26, 2018
Priority date	Jun 26, 2017
Publication date	Sep 9, 2025
Grant date	Sep 9, 2025

Abstract

A platform configured to predict type or family of an unknown drug candidate compound, the platform including: a living cell or a tissue; a detector that measures an indicator of a cellular response by the living cell or tissue upon exposure to the unknown drug candidate compound; a memory configured to store data related to the indicator of the cellular response detected by the detector from a library of drug types and/or families; and one or more processing unit(s) configured to: process the data related to the indicator of the cellular response of the living cell or tissue upon exposure to the unknown drug candidate compound, and compare cellular response data from the library of drug types and/or families, so that a drug type and/or a drug family and/or a mechanism of action of the unknown drug candidate compound can be predicted on the basis of a similarity between the detected cellular response data of the unknown drug candidate compound and the cellular response data of the library of drug types and/or families. Also disclosed are methods of screening an unknown drug, including: comparing the data measured from a test cell to corresponding cellular response data in a library of known drug types, and determining a relationship between the unknown drug and a known drug type or a known drug family to predict the type or family of the unknown drug.

First claim

What is claimed is:

1. A platform configured to detect cardioactivity of a drug candidate compound, the platform comprising:
(a) a living cell or a tissue that is capable of exerting a force in response to exposure to the drug candidate compound;
(b) a detector that measures onset, duration and magnitude of the force as a function of time by the living cell or tissue upon exposure to the drug candidate compound;
(c) a memory configured to store data related to the onset, duration and magnitude of the force detected by the detector; and
(d) one or more processing unit(s) configured to:
(i) employ machine learning, wherein a supervised learning algorithm uses a training set to teach one or more model(s) of cardioactivity, allowing the model(s) to learn over time to perform automated drug classification on novel data sets,
(ii) process the data related to the onset, duration and magnitude of the force as a function of time of the living cell or tissue upon exposure to the drug candidate compound,
(iii) consolidate a plurality of parameters pertaining to the onset, duration and magnitude of the force as a function of time into one or more index/indices of cardioactivity, and
(iv) compare the one or more index/indices of cardioactivity to known indices of cardioactivity to determine if the drug candidate compound is capable of modulating cardioactivity,
wherein the platform is configured to apply a plurality of electrical pacing frequencies to the living cell or tissue and wherein cellular response data elicited by the applied plurality of electrical pacing frequencies is captured and analyzed.

2. The platform according to claim 1 , wherein the living cell or tissue is a model of cardiac muscle fiber.

3. The platform according to claim 1 , wherein the living cell or tissue is configured as a human cardiac tissue strips (hCTS).

4. The platform according to claim 1 , wherein the machine learning utilizes predetermined parameters of onset, duration and magnitude of the force as a function of time to classify the response by the living cell or tissue to the drug.

5. The platform according to claim 4 , wherein the predetermined parameters of the onset, duration and magnitude of the force as a function of time comprise one or more of the following parameters:
(a) a prescribed pacing frequency;
(b) a captured pacing frequency;
(c) a maximum force generated (amplitude);
(d) a duration of rise from a cutoff level to maximum force in a contraction phase;
(e) a duration of decline from maximum force to a cutoff level in a relaxation phase;
(f) an area under the curve of rise from a cutoff level to maximum force;
(g an area under the curve of decline from maximum force to a cutoff level;
(h) a maximum change of force as a function of time (ΔF/Δt) of contraction phase; and
(i) a maximum change of force as a function of time (ΔF/Δt) of relaxation phase.

6. The platform according to claim 5 , wherein:
(d) the duration of the rise from the cutoff level to maximum force in the contraction phase is from 95% cutoff to max force (contraction phase),
(e) the duration of the decline from maximum force to the cutoff level in the relaxation phase is from max force to 95% cutoff (relaxation phase),
(f) the area under the curve of the rise from the cutoff level to maximum force is from 95% cutoff to max force,
(g) the area under the curve of the decline from maximum force to a cutoff level is from max force to 95% cutoff,
or wherein the predetermined parameters of the onset, duration and magnitude of the force as a function of time may further comprise one or more of the following parameters:
(j) a duration of rise from 50% cutoff to max force,
(k) a duration of decline from max force to 50% cutoff,
(l) an area under the curve of rise from 50% cutoff to max force,
(m) an area under the curve of decline from max force to 50% cutoff,
(n) a duration of rise from 25% cutoff to max force,
(o) a duration of decline from max force to 25% cutoff to max force,
(p) an area under the curve of rise from 50% cutoff to max force, and
(q) an area under the curve of decline from max force to 50% cutoff.

7. The platform according to claim 1 , wherein the measurement of onset, duration and magnitude of the force as a function of time comprises a measure of cell or tissue motion and/or electrical conduction and/or calcium flux and wherein the detector is capable of detecting motion and/or electrical conduction and/or calcium flux in the living cell or tissue following exposure to the drug.

8. The platform according to claim 7 , wherein the electrical conduction detected corresponds to one or more of a micro-impedance signal and an electrophysiological signal.

9. The platform according to claim 1 , wherein the processing unit is configured to output dosing information of the drug candidate compound to modulate cardioactivity based upon a comparison to cardioactivity data from one or more known compound(s).

10. The platform according to claim 1 , further comprising a library of drug types and/or families and corresponding measures of cardioactivity of the library of drug types and/or families stored in the memory.

11. The platform according to claim 10 , wherein each drug type or drug family is characterized by a plurality of distinct compounds within the drug type or drug family.

12. A method of screening a drug to determine cardioactivity of the drug using the platform according to claim 1 , the method, comprising:
(a) exposing a test cell or a tissue to the drug,
(b) applying a plurality of electrical pacing frequencies to the test cell or the tissue and wherein cellular response data elicited by the applied plurality of electrical pacing frequencies is captured and analyzed,
(c) quantifying data relating to onset, duration and magnitude of a force as a function of time measured from the test cell in response to the drug,
(d) comparing the data measured from the test cell or the tissue to corresponding data relating to onset, duration and magnitude of the force as a function of time in a library of known drug types, and
(e) determining cardioactivity of the drug.

13. The method of claim 12 , wherein the data measured from the test cell is indicative of cardiotoxicity.

14. The method of claim 12 wherein the test cell or tissue is a human cardiac tissue construct.

15. The method according to claim 12 , wherein a degree to which a compound is cardiotoxic/cardioactive is determined.

16. The method according to claim 12 , wherein machine learning is used to form predetermined parameters of the onset, duration and magnitude of the force as a function of time indicative of cardioactivity of known drug types.

17. The method according to claim 12 , wherein said comparing the data relating to onset, duration and magnitude of the force as a function of time of the test cell to corresponding data of known drug types is done by a series of binary classifications.

18. The method according to claim 12 comprising calculating a singular quantitative index generated by a supervised learning algorithm to consolidate a plurality of parameters of data relating to onset, duration and magnitude of the force as a function of time into a singular quantitative index.

19. The method of claim 12 , wherein the drug candidate is determined to be a Ca 2+ channel blocker; an adrenergic agonist; a cardiac glycoside; an hERG K + channel blocker; an ACE inhibitor or a non-cardioactive, nonsteroidal ant-inflammatory drug.

Assignees

• Univ California
• Novoheart International Ltd

Inventors

• Khine Michelle
• Lee Eugene
• Li Tang Wai Ronald Adolphus
• Tran David Dan

Classifications

• G01N30/8675

  Evaluation, i.e. decoding of the signal into analytical information (for analysis of specific compounds see also G01N30/88 and subgroups of G01N33/00; chemical libraries per se C40B) · CPC title

• G16C20/30

  Prediction of properties of chemical compounds, compositions or mixtures · CPC title

• G16C20/64

  Screening of libraries · CPC title

• G16C20/70

  Machine learning, data mining or chemometrics · CPC title

• G16C20/20

  Identification of molecular entities, parts thereof or of chemical compositions · CPC title