[go: up one dir, main page]

EP1671250A2 - Procede pour determiner un dosage de substance active - Google Patents

Procede pour determiner un dosage de substance active

Info

Publication number
EP1671250A2
EP1671250A2 EP04765437A EP04765437A EP1671250A2 EP 1671250 A2 EP1671250 A2 EP 1671250A2 EP 04765437 A EP04765437 A EP 04765437A EP 04765437 A EP04765437 A EP 04765437A EP 1671250 A2 EP1671250 A2 EP 1671250A2
Authority
EP
European Patent Office
Prior art keywords
model
specific
pbpk
data
active
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.)
Ceased
Application number
EP04765437A
Other languages
German (de)
English (en)
Inventor
Walter Schmitt
Stefan Willmann
Edgar Diessel
Ingmar Dorn
Jens Burmeister
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.)
Bayer AG
Original Assignee
Bayer Technology Services GmbH
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 Bayer Technology Services GmbH filed Critical Bayer Technology Services GmbH
Publication of EP1671250A2 publication Critical patent/EP1671250A2/fr
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P9/00—Drugs for disorders of the cardiovascular system
    • A61P9/04—Inotropic agents, i.e. stimulants of cardiac contraction; Drugs for heart failure
    • 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
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • 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

Definitions

  • the invention relates to a method for determining the individual dosage of medicaments, for which it is known that their effect is influenced by pharmacokinetics and / or pharmacodynamics which are dependent on individual factors of the patients.
  • the method can be used either as a point of care solution directly in the clinic or doctor's office or as a special method in laboratory medicine.
  • the therapeutic effect of medication is determined both by the intrinsic, biochemical effect of the active ingredient directly on the biological target molecule and by the concentration at the site of action.
  • concentration at the site of action is in turn influenced by various factors such as the amount absorbed during oral administration, distribution in the body and speed of metabolic breakdown and excretion. These processes depend heavily on the physiological and anatomical properties of the patient's body. The following are to be mentioned in detail:
  • the relationship between the individual properties of the body and the behavior of an active pharmaceutical ingredient is at least qualitatively known in many cases.
  • Special features of some influencing factors, such as body weight or blood flow rates, can be easily diagnosed by the attending doctor (weight) or through medical knowledge, e.g. about changes in blood flow in the presence of a disease, can be estimated.
  • the influence of body weight is taken into account in some cases by administering a weight-specific dose. In principle, the current methods only allow a qualitative consideration of the individual circumstances.
  • the function of the proteins can also be e.g. be influenced by a genetic change in the protein structure or interaction with other substances (see: J. Licinio, M. Wong (Eds.), “Pharmacogenomics”, Wiley-VCH, Weinheim 2002; AD Rodrigues, "Drug-Drug Interactions", Marcel Dekker, 2002).
  • SNPs single nucleotide polymorphisms
  • Genomic markers such as, for example, SNPs can be detected at the DNA level in the blood or other body fluids.
  • methods specifically designed for this detection for example biochips or PCR-based detection methods, are already available in development cover or on the market. This opens up the possibility of an adjusted dosage based on a corresponding DNA test directly in the doctor's office or as a laboratory method.
  • ADME-relevant proteins For many ADME-relevant proteins, information is available on the associated gene sequences that occur in humans and their modifications (see: SNP database available on the Internet at: http://www.ncbi.nlm.nih.gov/SNP/). The effect of the individual modifications on the ADME-relevant function is also known to a large extent (see: RG Tirona, RB Kim “Pharmacogenomics of Drug Transporters” in J. Licinio, M. Wong (Eds.), “Pharmacogenomics”, Wiley-VCH, Weinheim 2002).
  • a crucial problem in determining the optimal individual dosage is the simultaneous, complex dependence of the intracorporeal concentration on various influencing factors. While a single dependency can still be determined experimentally and used, for example, in tabular form for a dosage decision, this is generally only possible in terms of quality if there are several mutually influencing dependencies.
  • this problem can be solved by using a computer-aided simulation to calculate the concentrations.
  • a suitable method for this is the so-called physiology-based pharmacokinetic (PBPK) simulation, with which the uptake, distribution, metabolism and excretion behavior (ADME) of xenobiotics in the mammalian body can be described in detail based on the physiological requirements.
  • PBPK physiology-based pharmacokinetic simulation
  • the invention described here relates to a system from the combination of a detection system for determining the ADME-relevant genetic disposition of the patient, a PBPK / PD simulation and a database for substance properties (Fig.l), which is suitable for the individual concentration in a dose-related manner Calculate body and suggest the optimal individual dose from the result.
  • the invention relates to a method for determining the dosage of at least one active ingredient based on a genetic analysis, comprising the following steps:
  • the genetic test method (101) determines the patient's genetic disposition with regard to genes or proteins that are important for the ADME behavior of active substances.
  • Metabolizing enzymes especially monooxygenases of the Cytochrome P 450 family, phase II enzymes, which attach polar groups to the molecules to be excreted, active transporters, in particular multidrug resistance proteins e.g. P-Glycoprotein Family or Multidrug Resistance-Associated Proteins (MRP) or Organic Anion Transporting Polypeptide Family (OATP) or Organic Anion Transporter Family (OAT) or Organic Cation Transporter Family (OCT) or Novel Organic Cation Transporter Family (OCTN) or Peptide Transporter Family (PepT), or plasma binding proteins, particularly serum albumin and glycoproteins.
  • MRP Multidrug Resistance-Associated Proteins
  • OATP Organic Anion Transporting Polypeptide Family
  • OAT Organic Anion Transporter Family
  • OCT Organic Cation Transporter Family
  • OCTN Novel Organic Cation Transporter Family
  • Peptide Transporter Family Peptide Transporter Family
  • the drug-specific data are particularly preferably those which are selected from the series:
  • the characteristic patient data according to step e) are particularly preferably selected from the series:
  • the physiological influencing parameters according to step f) are particularly preferably selected from the series: Flow rate Q x of blood through organ X, volume V x of organ X or permeability surface product (PxSA x ) for organ X.
  • the PBPK model is preferably a simulation program which simulates at least the following functions: intestinal uptake, blood transport, distribution in organs by permeation or active transport, metabolism, excretion via urine or bile.
  • the invention further relates to a device for determining the dosage of active substances, in particular according to the method according to the invention described above, comprising at least one gene sequence-specific analysis device (101), a computer unit connected thereto with a program comprising a pharmacokinetic model (108), a knowledge database (105) and input modules (104) for patient data, characterized in that the PBPK model (108) is used as the pharmacokinetic model (108).
  • a device for determining the dosage of active substances in particular according to the method according to the invention described above, comprising at least one gene sequence-specific analysis device (101), a computer unit connected thereto with a program comprising a pharmacokinetic model (108), a knowledge database (105) and input modules (104) for patient data, characterized in that the PBPK model (108) is used as the pharmacokinetic model (108).
  • ADME-relevant proteins are:
  • Metabolizing enzymes monooxygenases of the cytochrome P 450 family; Phase II enzymes attach the polar groups to the molecules to be secreted.
  • Multidrug Resistance P-Glycoprotein Family
  • MRP Multidrug Resistance-Associated Proteins
  • OATP Organic Anion Transporting Polypeptide Family
  • OAT Organic Anion Transporter Family
  • OCT Organic Cation Transporter Family
  • OCTN Novel Organic Cation Transporter Family
  • Peptide Transporter Family Peptide Transporter Family
  • the gene test method (101) itself can be, for example, a method for the direct determination of the expression of the relevant proteins in the organ tissue, the transcription of relevant RNA molecules or a method for the detection of SNPs of the DNA from samples of body fluids. It is preferably a biochip or PCR-based application.
  • the results of the gene test are evaluated using a test-specific method (102) in order to obtain the required information about the influence of processes relevant to ADME.
  • the expression level of the proteins is determined either directly or, in the case of DNA analysis, via known relationships, the effect on the function or expression of the corresponding protein is determined.
  • Genomic markers such as SNPs can also be used to divide patients into specific groups such as rapidly or slowly metabolizing patients are used. Genomic markers are currently also being sought, which enable patients to be classified into responders / non-responders or patients with and without expected side effects related to certain drugs or groups of drugs.
  • the data record (103) thus obtained is transferred to the PBPK / PD model (108) as input data.
  • Patient-specific data relevant for dose calculation (104) must be entered manually.
  • This data is information that can be obtained through measurement, exploration or anamnesis. Some examples are: body weight or body surface area, body fat content, age, gender.
  • the parameter values of the PBPK / PD model resulting from this data are calculated in a subsequent step (106) with the aid of a knowledge database (105) about the underlying relationships.
  • This knowledge database can e.g. also contain information on the influence of certain diseases on ADME-relevant processes.
  • a possible embodiment of the module for the manual input of the patient data could be an input device with a menu-guided user interface, which dynamically adapts, depending on the information entered, requests further required data.
  • the drug-specific data for the medication to be administered which are required for the simulation of the ADME behavior, are stored in a further database (107).
  • These data are the parameter values contained in the PBPK / PD model, which depend on the physico- and biochemical properties of the active substance. These were previously determined directly experimentally or by adapting the simulation model to pharmacokinetic and / or pharmacodynamic data. Examples of this data are organ / blood Partition coefficients, membrane permeabilities and the kinetic constants of the metabolic processes and the active transport processes.
  • the central unit of the system is the PBPK PD simulation model with which the intracorporeal concentrations are actually calculated.
  • the typical structure of a PBPK model is shown in FIG. 2.
  • the basic procedure is to subdivide the body into individual compartments and to describe the exchange of active substance between these compartments with the help of mass conservation equations 7) .
  • the individual organs are sensibly chosen as compartments. If necessary, parts of the. Organs are defined as sub-compartments if either the mass transfer between them can be limited or information about concentration has to be obtained separately.
  • V x volume of organ X
  • C ar concentration of the substance that reaches the organ through the arterial blood
  • K x distribution coefficient of the substance between blood and organ X in equilibrium
  • the distribution into the individual organs is limited by the fact that they permeate through the cell membranes more slowly than they transport into the organ via the blood become.
  • the organs are divided into different sub-compartments separated by membranes and a model corresponding to FIG. 3 is obtained.
  • the sub-compartments to be considered are plasma volume (301), red blood cells (302), interstitial volume of the organ tissue (304) and cell volume of the organ tissue (306). Red blood cells and cells of the organ tissue are surrounded by membranes (303), (305) through which the active substance molecules must pass.
  • permeation terms according to Fick's 2nd law must be taken into account in the mass conservation equations for mass transport.
  • V x cell volume of organ X
  • K x distribution coefficient of the substance between blood and organ X in equilibrium
  • PxSA x permeability surface product for the organ x
  • the active processes of metabolism and active transport can be taken into account, for example, using so-called Michaelis-Menten terms, which describe the kinetics of the biochemical reactions.
  • Inclusion of active transport requires a permeation-limited model, as described above.
  • a detailed organ model including the active processes is shown in Fig. 4.
  • One or more metabolic processes (401) cause a decrease in the concentration of the original substance.
  • the active transport processes (402), (403) are described in such a way that they effect the transport of active substance molecules across the cell membrane, parallel to the passive permeation process. With these processes it should be noted that inward (402) must be distinguished from outward (403).
  • equation 2 is to be modified as follows. dC cell, metabol
  • ⁇ Binding constant of the active ingredient on the protein that causes process y
  • V x cell volume of organ X
  • K x distribution coefficient of the substance between blood and organ X in the state of equilibrium
  • PxSA x permeability surface product for the organ x
  • organs with more specific functions e.g. B. the gastrointestinal tract, the kidney, or the biliary tract.
  • additional parameters that describe the special physiological functions must be taken into account.
  • the local variation of sizes such as PxSA and pH value of the intestinal content must also be taken into account.
  • concentration-time relationship in the compartment which contains the biological target of the active ingredient can also be linked to a pharmacodynamic effect.
  • Typical effect functions are e.g. B .:
  • E 0 basic value of the pharmacological active parameter
  • Effect pharmacological active parameters (time-dependent)
  • E 0 basic value of the pharmacological active parameter
  • ß parameter for the increase in the effect as a function of the concentration
  • the operation of the entire system for individual dose calculation is now as follows.
  • the individual values of the parameters of the PBPK / PD model which depend on the physiology or anatomy, must be determined.
  • the results of the gene test (101) are evaluated and the proteins identified in which deviations from the normal population are to be expected with regard to expression or function (102).
  • the expression or the effectiveness is then determined for the organs concerned via known and stored relations and the v max and k m values are calculated accordingly.
  • the parameters V, Q, K, PxSA as well as other descriptions of special organs are created with the help of the related relationships stored in the knowledge database (105).
  • gastrointestinal tract, kidney etc. are determined.
  • the standard values of the drug-specific parameters stored in the drug database (107) are taken into account, which are then modulated according to the individual circumstances. If necessary, the genetic or disease-related influence on properties such as the composition of the cell membranes and pH values of individual compartments must also be taken into account, which can also influence permeability PxSA and distribution coefficient K.
  • the pharmacokinetics of the active ingredient to be administered are simulated using the standard dosage. According to rules that are dependent on the active ingredient and the goal of the therapy and are also stored in the active ingredient database, it is then decided on the basis of the calculated concentration profiles and, if appropriate, the resulting pharmacodynamic effect, whether an adjustment of the dose is necessary. If this is the case, a more suitable dose is suggested. This is done by linear conversion to those in the body The optimal dose reached is determined if one is in the dose linear pharmacokinetic or pharmacodynamic regime. If this is not the case, automatic, gradual change of the dose in the simulation adjusts this to the optimum. The result of these optimizations is output by the system and can then be used to determine the dose.
  • Fig. 1 Basic structure of the entire system for determining the individual dosage.
  • Fig. 2 Schematic diagram of the structure of the physiology-based pharmacokinetic (PBPK) simulation model 3: Composition of an organ in the PBPK model.
  • PBPK physiology-based pharmacokinetic
  • Fig. 4 Principle of the description of active transporters and metabolization processes in the PBPK model.
  • Fig. 5 Simulated concentration-time curve (line) in the blood plasma of patients with CC polymorphism in exon 26 (C3435T) compared to experimentally determined values (points).
  • Fig. 6 As Fig. 5. Additional concentration curve for patients with TT Polymo ⁇ hismus in exon 26 (C3435T) (gray line).
  • Table 3 Composition of the organs according to FIG. 3
  • the following is an example that shows how simulations can be used to describe the influence of genetic disposition for active transporters on the pharmacokinetics of active substances.
  • P-Gp p-glycoprotein
  • MDRI p-glycoprotein
  • MDRl can be present in various alleles, it being known that these lead to different activity of the associated protein (see Martin F. Fromm, The influence of MDR 1 polymorphisms on P-glycoprotein expression and function in humans, Advanced Drug Delivery Reviews 54, 1295 (2002)).
  • the parameter set listed in Tables 2-6 is stored in the database with active ingredient information (107).
  • the estimation of the influence of, for example, differing MDRI sequences can then be achieved by changing the parameters of the simulation model affected thereby.
  • This change is carried out taking into account the expression data (103) in the step "parameter determination" (106).
  • An example of a data set in which the expression level of P-Gp in the intestinal wall was determined as a function of the MDRI polymorphism is inS. Hoffmeyer (2000).
  • the expression level in turn determines the maximum speed Vmax of the transport process.
  • the knowledge database (105) therefore contains the assignment of relative Vmax values to the gene sequences of the different polymorphisms in the application case, which, together with the substance-specific absolute Vmax values from the database with active ingredient information (107), are to be used in the PBPK model (108) Parameters.
  • the homozygous type TT can be simulated by reducing Vmax by 51% in accordance with the lower expression level. The corresponding result is shown in FIG. 6. The resultant increase in the simulation of Cmax by 45%, which corresponds to the experimentally found increase of 30% (see Table 1).
  • the cmax values resulting from the simulation of the type TT would be compared with the safety-critical values contained in the database with active ingredient information (107) and a reduced dosage would be suggested if necessary.
  • To determine the suggested dosage e.g. Simulations with iteratively changed doses carried out until the pharmacokinetic parameters are in the safe range.
  • the suggested dosage can also be determined by linear conversion.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Medical Informatics (AREA)
  • Theoretical Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Evolutionary Biology (AREA)
  • Biotechnology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Medicinal Chemistry (AREA)
  • Public Health (AREA)
  • Cardiology (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Veterinary Medicine (AREA)
  • Hospice & Palliative Care (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Organic Chemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Pharmaceuticals Containing Other Organic And Inorganic Compounds (AREA)
  • Steroid Compounds (AREA)

Abstract

La présente invention concerne un procédé pour déterminer un dosage d'au moins une substance active sur la base d'une analyse génétique. Le procédé comprend les étapes suivantes: a) analyse (101) de séquences géniques spécifiques, au moyen d'un appareil d'analyse spécifique aux séquences géniques, en particulier d'un détecteur spécifique aux séquences, ou détermination de l'expression de protéines soit par transcription d'ARN au moyen de procédés de détection quantitatifs ARN-spécifiques, soit par mesure directe de l'expression protéique par un appareil d'analyse protéique; b) association des séquences géniques à des fonctions physiologiques du corps humain ou animal, notamment des fonctions physiologiques qui ont une influence sur la dégradation, l'absorption, l'évacuation ou le comportement de répartition de la substance active dans le corps; c) transmission des données génétiques et des données d'association à un modèle pharmacocinétique basé sur la physiologie (modèle PBPK) (108); d) entrée de données spécifiques de substance active dans le modèle PBPK (108); e) entrée de données caractéristiques du patient, éventuellement à partir de mesures effectuées directement sur le corps; f) calcul de paramètres physiologiques d'influence nécessaires au modèle PBPK, à partir des données relatives au patient, par utilisation d'informations contenues dans une banque de données connues, et transmission des paramètres au modèle PBPK (108); g) calcul de la dose individuelle à partir des données des étapes c), d) et f), par utilisation du modèle PBPK (108).
EP04765437A 2003-10-02 2004-09-21 Procede pour determiner un dosage de substance active Ceased EP1671250A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE10345837A DE10345837A1 (de) 2003-10-02 2003-10-02 Verfahren zur Bestimmung einer Wirkstoffdosierung
PCT/EP2004/010560 WO2005033334A2 (fr) 2003-10-02 2004-09-21 Procede pour determiner un dosage de substance active

Publications (1)

Publication Number Publication Date
EP1671250A2 true EP1671250A2 (fr) 2006-06-21

Family

ID=34353270

Family Applications (1)

Application Number Title Priority Date Filing Date
EP04765437A Ceased EP1671250A2 (fr) 2003-10-02 2004-09-21 Procede pour determiner un dosage de substance active

Country Status (7)

Country Link
US (1) US20050074803A1 (fr)
EP (1) EP1671250A2 (fr)
JP (1) JP2007510970A (fr)
AU (1) AU2004278478A1 (fr)
CA (1) CA2540789A1 (fr)
DE (1) DE10345837A1 (fr)
WO (1) WO2005033334A2 (fr)

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8949032B2 (en) * 2002-03-29 2015-02-03 Genomatica, Inc. Multicellular metabolic models and methods
DE102004010516A1 (de) 2004-03-04 2005-09-22 Bayer Technology Services Gmbh Verbessertes Verfahren zur zeitlichen Dosierung von Arzneistoffen
DE102005028080A1 (de) * 2005-06-17 2006-12-21 Bayer Technology Services Gmbh Verfahren zur zeitlich gesteuerten intravenösen Verabreichung des Narkosemittels Propofol
CA2911569C (fr) * 2005-11-29 2019-11-26 Children's Hospital Medical Center Optimisation et personnalisation de selection et de dosage de medicaments
JP2007279999A (ja) * 2006-04-06 2007-10-25 Hitachi Ltd 薬物動態解析システム及び方法
DE102006028232A1 (de) * 2006-06-20 2007-12-27 Bayer Technology Services Gmbh Vorrichtung und Verfahren zur Berechnung und Bereitstellung einer Medikamentendosis
WO2008081830A1 (fr) * 2006-12-27 2008-07-10 Nemoto Kyorindo Co., Ltd. Dispositif d'injection de médicament liquide et procédé d'injection de médicament liquide
US20080221847A1 (en) * 2007-03-09 2008-09-11 Frederique Fenetteau Method of developing a pharmacokinetic profile of a xenobiotic disposition in a mammalian tissue
US20100331827A1 (en) * 2008-02-18 2010-12-30 Koninklijke Philips Electronics N.V. Administration of drugs to a patient
US20100125421A1 (en) * 2008-11-14 2010-05-20 Howard Jay Snortland System and method for determining a dosage for a treatment
US20100125782A1 (en) * 2008-11-14 2010-05-20 Howard Jay Snortland Electronic document for automatically determining a dosage for a treatment
EP2538360A1 (fr) * 2011-06-16 2012-12-26 Koninklijke Philips Electronics N.V. Procédé de prédiction d'une valeur à risque pour une dilution sanguine
WO2015017449A1 (fr) * 2013-07-29 2015-02-05 The Regents Of The University Of California Plate-forme de technologie de commande de système à rétroaction en temps réel avec stimulations dynamiquement modifiées
WO2015017798A2 (fr) 2013-08-02 2015-02-05 CRIXlabs, Inc. Procédé et système de prédiction des répartitions spatiales et temporelles de vecteurs de substances thérapeutiques
EP3236956A1 (fr) 2014-12-23 2017-11-01 Bosteels, Arnaud Combinaison de rémifentanil et de propofol
WO2019104101A1 (fr) * 2017-11-21 2019-05-31 Verisim Life Inc. Systèmes et procédés pour une circulation dans le corps entier et une prédiction de concentration de médicament
CN113140321B (zh) * 2021-05-20 2023-12-19 中国药科大学 运用PK-sim预测异甘草酸镁在人体中暴露浓度的方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5424186A (en) * 1989-06-07 1995-06-13 Affymax Technologies N.V. Very large scale immobilized polymer synthesis
AUPR446701A0 (en) * 2001-04-18 2001-05-17 Gene Stream Pty Ltd Transgenic mammals for pharmacological and toxicological studies
US20030104428A1 (en) * 2001-06-21 2003-06-05 President And Fellows Of Harvard College Method for characterization of nucleic acid molecules

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO2005033334A2 *

Also Published As

Publication number Publication date
CA2540789A1 (fr) 2005-04-14
WO2005033334A2 (fr) 2005-04-14
JP2007510970A (ja) 2007-04-26
US20050074803A1 (en) 2005-04-07
AU2004278478A1 (en) 2005-04-14
WO2005033334A3 (fr) 2005-09-29
DE10345837A1 (de) 2005-04-21

Similar Documents

Publication Publication Date Title
EP1671250A2 (fr) Procede pour determiner un dosage de substance active
Graham et al. Fluctuating asymmetry of human populations: A review
Sjölund et al. Quantitative and qualitative antimicrobial usage patterns in farrow-to-finish pig herds in Belgium, France, Germany and Sweden
WO2006133825A1 (fr) Dispositif d'administration intraveineuse regulee dans le temps de l'agent anesthesique propofol
Suckling et al. Altered dietary salt intake for preventing and treating diabetic kidney disease
Beery Antisocial oxytocin: complex effects on social behavior
Hamida et al. Increased alcohol seeking in mice lacking Gpr88 involves dysfunctional mesocorticolimbic networks
Petzold et al. Baseline impulsivity may moderate L-DOPA effects on value-based decision-making
EP2035985A1 (fr) Procédé et dispositif de calcul et de préparation d'une posologie
Zetian et al. Pig-vet: a web-based expert system for pig disease diagnosis
J Diaz et al. Role of statistical random-effects linear models in personalized medicine
EP1722839B1 (fr) Appareil de dosage chronometrique de medicaments
Gaudillière Better prepared than synthesized: Adolf Butenandt, Schering Ag and the transformation of sex steroids into drugs (1930–1946)
WO2005033982A2 (fr) Procede pour simuler l'interaction de substances chimiques avec des organismes vivants
Adams et al. Age-related synapse loss in hippocampal CA3 is not reversed by caloric restriction
Huppertz et al. Micturition in Göttingen minipigs: first reference in vivo data for urological research and review of literature
Lattin et al. In vivo imaging of D2 receptors and corticosteroids predict behavioural responses to captivity stress in a wild bird
Maier et al. Alcohol and nutritional control treatments during neurogenesis in rat brain reduce total neuron number in locus coeruleus, but not in cerebellum or inferior olive
Zhang et al. Identification of gene markers based on well validated and subcategorized stressed animals for potential clinical applications in PTSD
WO2022069162A1 (fr) Détermination de patients comparables sur la base d'ontologies
Zijlmans et al. Overweight management through mild caloric restriction in multigenerational long-tailed macaque breeding groups
Long et al. Use of 20% intravenous lipid emulsion for the treatment of loperamide toxicosis in a Collie homozygous for the ABCB1‐1∆ mutation
EP3469500A1 (fr) Procédé et système d'assistance à la prescription d'une médication pour un patient
Picasso et al. Virtual reconstructions of the endocranial cavity of Rhea americana (Aves, Palaeognathae): postnatal anatomical changes
Lammers et al. The impact of muscular dystrophy on limb bone growth and scaling in mice

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20060502

AK Designated contracting states

Kind code of ref document: A2

Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IT LI LU MC NL PL PT RO SE SI SK TR

17Q First examination report despatched

Effective date: 20060801

DAX Request for extension of the european patent (deleted)
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN REFUSED

18R Application refused

Effective date: 20080610