GB2583309A - Neuron model generation for personalised drug treatment - Google Patents

Neuron model generation for personalised drug treatment Download PDF

Info

Publication number
GB2583309A
GB2583309A GB2010618.3A GB202010618A GB2583309A GB 2583309 A GB2583309 A GB 2583309A GB 202010618 A GB202010618 A GB 202010618A GB 2583309 A GB2583309 A GB 2583309A
Authority
GB
United Kingdom
Prior art keywords
neuronal
neuronal models
ranges
models
patient
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
GB2010618.3A
Other versions
GB202010618D0 (en
Inventor
Rumbell Tim
Kozloski James
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
International Business Machines Corp
Original Assignee
International Business Machines Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by International Business Machines Corp filed Critical International Business Machines Corp
Priority claimed from PCT/IB2018/059697 external-priority patent/WO2019130135A1/en
Publication of GB202010618D0 publication Critical patent/GB202010618D0/en
Publication of GB2583309A publication Critical patent/GB2583309A/en
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Public Health (AREA)
  • Data Mining & Analysis (AREA)
  • Epidemiology (AREA)
  • Biophysics (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biotechnology (AREA)
  • Software Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Primary Health Care (AREA)
  • Mathematical Physics (AREA)
  • Analytical Chemistry (AREA)
  • Pathology (AREA)
  • Genetics & Genomics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Bioethics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medicinal Chemistry (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

A computer-implemented method for generating neuronal models for personalized drug treatment selection for a patient includes receiving allelic information for at least one neurophysiological coding region of a genome of the patient, and a physiological model of a disease associated with the genome. The method further includes determining a set of ion channels correlated with the allelic information, and receiving a set of phenotypic measurement ranges associated with the ion channels from the determined set. The method further includes performing a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding phenotypic measurement ranges, and analyzing the generated neuronal models to identify components that affect the physiological model. The method further includes selecting a drug for the patient based at least in part on the identified components.

Claims (20)

1. A computer-implemented method for generating neuronal models for personalized drug treatment selection for a patient, the method comprising: receiving allelic information for at least one neurophysiological coding region of a genome of the patient; receiving a physiological model of disease associated with a phenotype of the patient; determining, from an ion channel database, a set of ion channels correlated with the allelic information; receiving a set of physiological measurement ranges, each physiological measurement range associated with a corresponding ion channel from the determined set of ion channels; performing a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding physiological measurement ranges; analyzing the generated neuronal models to identify components that affect the physiological model of disease; and selecting a drug for the patient based at least in part on the identified components.
2. The computer-implemented method of claim 1 , wherein the set of physiological measurement ranges comprises ranges that correspond to neuronal models that generate healthy neuronal responses.
3. The computer-implemented method of claim 2, wherein the set of physiological measurement ranges further comprises ranges that correspond to neuronal models that generate diseased neuronal responses.
4. The computer-implemented method of claim 1 , wherein analyzing the generated neuronal models comprises performing a partial least square regression using the ion channel parameter values of the generated neuronal models and the physiological model.
5. The computer-implemented method of claim 1 , wherein the simulation to generate multiple neuronal models uses optimization comprising at least in part a soft thresholding of error values and a penalty term for crowdedness.
6. The computer-implemented method of claim 1 , wherein the physiological model of disease comprises at least two trait values.
7. The computer-implemented method of claim 6, wherein selecting the drug for the patient based at least in part on the identified components comprises accessing a drug database to identify drugs with ion channel alterations associated with the identified components.
8. A computer system for generating neuronal models for personalized drug treatment selection for a patient, the system comprising: a memory; and a processor communicatively coupled to the memory, the processor configured to: receive allelic information for at least one neurophysiological coding region of a genome of the patient; receive a physiological model of disease associated with a phenotype of the patient; determine, from an ion channel database, a set of ion channels correlated with the allelic information; receive a set of physiological measurement ranges, each physiological measurement range associated with a corresponding ion channel from the determined set of ion channels; perform a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding physiological measurement ranges; analyze the generated neuronal models to identify components that affect the physiological model of disease; and select a drug for the patient based at least in part on the identified components.
9. The system of claim 8, wherein the set of physiological measurement ranges comprises a first set of ranges that correspond to neuronal models that generate healthy neuronal responses, and a second set of ranges that correspond to neuronal models that generate diseased neuronal responses.
10. The system of claim 8, wherein analyzing the generated neuronal models comprises performing a partial least square regression using the ion channel parameter values of the generated neuronal models and the physiological model.
11. The system of claim 9, wherein analyzing the generated neuronal models comprises using a 2-gate hyperplane normal algorithm to determine a hyperplane between a first set of neuronal models corresponding to the first set of ranges and a second set of neuronal models corresponding to the second set of ranges.
12. The system of claim 8, wherein the simulation to generate multiple neuronal models uses optimization comprising at least in part a soft thresholding of error values combined with a penalty term for crowdedness.
13. The system of claim 8, wherein the physiological model of disease comprises at least two trait values.
14. The system of claim 13, wherein selecting the drug for the patient based at least in part on the identified components comprises accessing a drug database to identify drugs with ion channel alterations associated with the identified components.
15. A computer program product comprising a computer storage device having computer readable instructions stored therein, the computer readable instructions are executable by a processing unit for generating neuronal models for personalized drug treatment selection for a patient, the selection comprising: receiving allelic information for at least one neurophysiological coding region of a genome of the patient; receiving a physiological model of disease associated with a phenotype of the patient; determining, from an ion channel database, a set of ion channels correlated with the allelic information; receiving a set of physiological measurement ranges, each physiological measurement range associated with a corresponding ion channel from the determined set of ion channels; performing a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding physiological measurement ranges; analyzing the generated neuronal models to identify components that affect the physiological model of disease; and selecting a drug for the patient based at least in part on the identified components.
16. The computer program product of claim 15, wherein the set of physiological measurement ranges comprises a first set of ranges that represent neuronal models that generate healthy neuronal responses, and a second set of ranges that represent neuronal models that generate diseased neuronal responses.
17. The computer program product of claim 15, wherein analyzing the generated neuronal models comprises performing a partial least square regression using the ion channel parameter values of the generated neuronal models and the physiological model of disease.
18. The computer program product of claim 16, wherein analyzing the generated neuronal models comprises using a 2-gate hyperplane normal algorithm to determine a hyperplane between a first set of neuronal models corresponding to the first set of ranges and a second set of neuronal models corresponding to the second set of ranges.
19. The computer program product of claim 15, wherein the simulation to generate multiple neuronal models uses optimization comprising at least in part a soft thresholding of error values combined with a penalty term for crowdedness.
20. The computer program product of claim 15, wherein the physiological model comprises at least two trait values, and wherein selecting the drug for the patient based at least in part on the identified components comprises accessing a drug database to identify drugs with ion channel alterations associated with the identified components.
GB2010618.3A 2017-12-28 2018-12-06 Neuron model generation for personalised drug treatment Withdrawn GB2583309A (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201715865582A 2017-12-28 2017-12-28
PCT/IB2018/059697 WO2019130135A1 (en) 2017-12-28 2018-12-06 Neuron model generation for personalised drug treatment

Publications (2)

Publication Number Publication Date
GB202010618D0 GB202010618D0 (en) 2020-08-26
GB2583309A true GB2583309A (en) 2020-10-21

Family

ID=72140018

Family Applications (1)

Application Number Title Priority Date Filing Date
GB2010618.3A Withdrawn GB2583309A (en) 2017-12-28 2018-12-06 Neuron model generation for personalised drug treatment

Country Status (1)

Country Link
GB (1) GB2583309A (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009152484A2 (en) * 2008-06-13 2009-12-17 Izumi Bio, Inc. Methods and platforms for drug discovery
CN101658522A (en) * 2008-08-26 2010-03-03 普尔药物科技开发（深圳）有限公司 Application of tacrine short-chain dimer in preparation of medicament for treating neurodegenerative diseases
CN105998316A (en) * 2016-05-23 2016-10-12 齐齐哈尔医学院 Traditional Chinese medicine composition for treating alzheimer's disease, and preparation method and application thereof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009152484A2 (en) * 2008-06-13 2009-12-17 Izumi Bio, Inc. Methods and platforms for drug discovery
CN101658522A (en) * 2008-08-26 2010-03-03 普尔药物科技开发（深圳）有限公司 Application of tacrine short-chain dimer in preparation of medicament for treating neurodegenerative diseases
CN105998316A (en) * 2016-05-23 2016-10-12 齐齐哈尔医学院 Traditional Chinese medicine composition for treating alzheimer's disease, and preparation method and application thereof

Also Published As

Publication number Publication date
GB202010618D0 (en) 2020-08-26

Similar Documents

Publication Publication Date Title
Zhang EEG signals classification using machine learning for the identification and diagnosis of schizophrenia
McCracken et al. Decentering, rumination, cognitive defusion, and psychological flexibility in people with chronic pain
Shin et al. Cognitive functioning in obsessive-compulsive disorder: a meta-analysis
Danilenko et al. The hockey-stick method to estimate evening dim light melatonin onset (DLMO) in humans
US20080256006A1 (en) Clinical Trial Phase Simulation Method and Clinical Trial Phase Simulator For Drug Trials
JP2018507470A (en) System and method for predicting response to chemotherapy for high-grade bladder cancer
Kupers et al. Asymmetries around the visual field: From retina to cortex to behavior
Crawford et al. Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth-death processes
Shuman et al. Associations between muscle synergies and treatment outcomes in cerebral palsy are robust across clinical centers
Wijenberg et al. Explaining fatigue in multiple sclerosis: cross-validation of a biopsychosocial model
Escudero et al. Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials
Bosco et al. Reference frames for reaching when decoupling eye and target position in depth and direction
Mayo et al. Incorporating big data into treatment plan evaluation: Development of statistical DVH metrics and visualization dashboards
Bootsma et al. The role of task difficulty in learning a visuomotor skill
Ergo et al. Oscillatory signatures of reward prediction errors in declarative learning
Hammond‐Kenny et al. Behavioural benefits of multisensory processing in ferrets
Kaiser et al. Rapid contextualization of fragmented scene information in the human visual system
Cruz-Garza et al. EEG-based investigation of the impact of classroom design on cognitive performance of students
Karim et al. Prior and present evidence: how prior experience interacts with present information in a perceptual decision making task
JP2021509198A5 (en)
GB2583309A (en) Neuron model generation for personalised drug treatment
Fernandez Rubio et al. The spatiotemporal dynamics of recognition memory for complex versus simple auditory sequences
Olsbo et al. Development and evaluation of spatial point process models for epidermal nerve fibers
Xiao et al. Normalization of neuronal responses in cortical area MT across signal strengths and motion directions
US20210398672A1 (en) Big Data-Driven Personalized Management of Chronic Pain

Legal Events

Date Code Title Description
WAP Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)