WO2005116854A2 - Verfahren zur (zweistufigen) dosis- und dosierungsfindung - Google Patents
Verfahren zur (zweistufigen) dosis- und dosierungsfindung Download PDFInfo
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- WO2005116854A2 WO2005116854A2 PCT/EP2005/005315 EP2005005315W WO2005116854A2 WO 2005116854 A2 WO2005116854 A2 WO 2005116854A2 EP 2005005315 W EP2005005315 W EP 2005005315W WO 2005116854 A2 WO2005116854 A2 WO 2005116854A2
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
Definitions
- the invention relates to a method for dosing specific doses and temporal dosing profiles of medicinal substances (in animals and humans) and agrochemicals (in the treatment of plants).
- the concentration-time profile of the active substance at the site of action alone is not predictive of the success of the therapy, since the therapeutic effect (or undesirable side effects) is determined by the complex kinetics and dynamics of biochemical processes. Without detailed knowledge of the mechanisms of action and side effects, sensible therapy optimization can no longer be carried out.
- the biological effect of an active ingredient and other chemical substances is determined by the temporal course of the substance concentration at the site of action and the biochemical interactions at the site of action.
- a prediction of effects is therefore only possible if predictive models of substance uptake, distribution, metabolism and excretion (so-called ADME models of absorption, distribution, metabolism, excretion), which can predict concentrations at any point in an organism, are combined are using models of the bio-chemical mechanism of action that can describe or predict the effect of a chemical substance in the organism.
- ADME models for a wide variety of organisms are known and state of the art.
- PBPK models physiology-based pharmacokinetic models
- PBPK models pharmacokinetic models
- Models for predicting the effect of a chemical substance at a site of action are also known and state of the art.
- models for the dynamic simulation of metabolic networks and signal transduction networks are of particular interest for the present invention.
- models of the binding behavior of chemical substances with the body's own molecules e.g. Transport proteins such as PGP or enzymes such as the family of P450 cytochromes, which play a decisive role in the distribution in the organism and the biotransformation and thus the breakdown of molecules.
- the predictive method described in the present application for determining the optimal dosage is able to differentiate between individual individuals in the to take into account the pharmacokinetic and pharmacodynamic behavior of an administered substance.
- the latter is achieved through models for predicting the effect of a chemical substance at its point of action.
- the method can already be used in the planning of clinical studies. In addition to improving the benefit / risk ratio for the individual test subjects, this can reduce the number of clinical examinations and their duration, while at the same time significantly increasing the likelihood of a successful study result.
- the method can also be used in clinical practice for the individualized optimization of therapies.
- the use of the method can be expected to reduce the cost of medical treatment and shorten the time of illness.
- the method can be used both for veterinary applications and for agrochemical issues (in the treatment of plants).
- the method is also able to provide estimates of maximum exposures (doses and duration of exposure) for toxins. These can be used in the context of chemical approvals to plan experimental studies and to validate the evaluation of experimental findings.
- the present invention is based on overcoming the complexity problem created by the integration by largely separating the two model components by means of an iterative calculation of the concentration and activity profiles of administered substances.
- the causal chain an active ingredient is administered, then finds a certain concentration at the site of action and consequently unfolds its effect
- the complex optimization problem of finding dosages to achieve a desired effect breaks down into two simpler optimization steps that can be treated mathematically (see Figure 2):
- Step 1 Determination of one or more suitable, ideally optimal, concentration-time profiles for one or more substances at one or more sites of action in order to achieve the greatest possible agreement with the desired effects.
- optimal concentrations of one or more substances must be determined for each effect.
- This optimization step is carried out with one or more predictive biological models that can be coupled with one another. The optimization of the effects can neither in a joint optimization process or independently of one another (see Figures 2.1, 3, 4, 5, 6).
- Step 2 Determination of an optimal dosage for one or more substances in order to achieve the greatest possible agreement with the optimal concentration-time profiles that were determined in step 1.
- This optimization step is carried out with one or more detailed ADME models (e.g. PBPK models), which can be coupled with one another.
- the optimization can be done for each of the models independently of one another or in a joint optimization process (see Figures 2.2, 7, 8, 9, 10).
- the dosage profiles obtained in this way are administered either manually or with the aid of a dosing device.
- all types of administration of active substances are conceivable.
- these types of administration are conceivable for both humans and animals.
- the active substance can be added to the water of an aquarium or other container in which the fish or fish are kept.
- Dosing devices mean all apparatuses to which a dose profile, either as a constant dose value or as a time-variable dose profile, can be specified.
- Infusion machines are particularly conceivable for medical applications.
- technical devices for enriching the breathing air with a gas or aerosol are conceivable.
- Veterinary applications can also be automated machines that automatically feed feed or that add an active ingredient to the water of a fish aquarium or fish tank.
- crop protection applications in addition to manual processes, all types of application of crop protection agents, including automatic spraying machines for mobile and stationary use in greenhouses or in fields, can be used for dosing.
- the method is of construction suitable for the treatment of simultaneous administration of several active substances which interact in their pharmacokinetic behavior and their action and the simultaneous consideration of (desired) effects such as (undesirable) side effects. Furthermore, the treatment of one or more effective or ineffective starting substances ("prodrugs”), which through metabolism in the body to one or more Other active substances (“metabolites”) can be transformed with this method in a simple manner.
- FIG. 2 A schematic representation of the method according to the invention (in its simplest form) is shown in Figure 2.
- the optimization of the local concentration of a substance carried out in step 1 is shown in the left part of the figure (2.1).
- the optimization of the dosage of the substance carried out in step 2 is shown in the right part of the figure (2.2).
- the process starts with a freely selectable start-concentration-time profile for the active substance in question at the site of action ( Figure 2, 2.3), which is used as an input function for the biological effect model ( Figure 2, 2.4).
- the biological mode of action can be adapted to parameters that were determined using technical-diagnostic methods and are either characteristic for the indication or for the individual patient or organism. All biological, biometric, chemical or physical methods that are able to determine model parameters, e.g. information obtained from a biopsy about the type of tumor affecting a patient could be used to individualize the effect model for that patient. Furthermore, e.g. Information about the size and morphology of a tumor, which was determined using imaging methods, can be used for individualization.
- model parameters by means of literature research, in particular also using bioinformatic tools for searching in literature, chemistry, gene, protein or also signal transduction network databases, is a possible variant of the method.
- bioinformatic tools for searching in literature, chemistry, gene, protein or also signal transduction network databases is a possible variant of the method.
- free parameters of the models that should not or cannot be individualized can be found.
- the effect model then calculates the
- ⁇ fil induced effect ( Figure 2, 2.5).
- this is compared with a target effect specified by the indication ( Figure 2, 2.6). If the target effect and the actual effect correspond, or if the deviation between the two falls below a threshold that is either predefined (resulting from biological boundary conditions, for example) or determined from the optimization process (from a numerical criterion), the concentration-time profile used as the input function in 2.3 becomes the target -Concentration time profile recorded ( Figure 2, 2.8) and step 1 ( Figure 2, 2.1) ended.
- the difference between the two can be suitable measure can be quantified. This measure can be, for example, a continuous quantity such as a distance square or, for example, also a discrete quantity such as the number of violations of a criterion.
- an optimization step is carried out ( Figure 2, 2.7) in which the input profile ( Figure 2, 2.3) is changed.
- All known numerical and analytical optimization methods can be used as methods for carrying out the optimization.
- gradient methods e.g. Newton or Quasi-Newton methods
- gradient-free methods e.g. interval nesting
- stochastic methods e.g. Monte Carlo methods
- evolutionary methods e.g. genetic optimization
- the particular execution of an analytical optimization process can result from the type of effect model used. All individual steps are repeated iteratively until a match between target effect and actual effect is achieved in 2.6 and step 1 can be canceled in 2.8.
- both effects and side effects can be treated, e.g. Upper limits are defined and the lower limit is set as the termination criterion.
- the target concentration-time profile (2.8) obtained in the first step serves as the target profile for the optimization of the dosing schedule in the second step ( Figure 2, 2.2) ( Figure 2, 2.9).
- Step 2 starts with a freely selectable starting dosing scheme ( Figure 2, 2.9).
- the ADME model e.g. a PBPK model
- the concentration-time profile resulting from this dosing scheme is calculated at the site of action ( Figure 2, 2.1 1).
- the ADME model can be adapted and individualized with the help of information about the indication and the active ingredient as well as with the physiological, anatomical or genetic properties of the individual patient or organism. For example, with a PBPK model, adjustments could be made for body size, body weight and body mass index.
- Information about the type (e.g. superficial, infiltrating, encapsulated), position and size of a tumor that is supposed to be the place of action of a treatment could also be used if it was collected using imaging methods, for example. If, for example, information is available about the genotype of a patient, which influences, for example, the expression of transport proteins, this could also be used for individualization.
- all technical-diagnostic methods ie all biological, biometric, chemical or physical analysis methods and methods, which are able to determine model parameters can be used.
- the acquisition of model parameters by means of literature research, especially with bioinformatic tools for searching in literature, chemistry, gene, protein or also signal Transduction network databases are a possible variant of the procedure. With the help of these methods, free parameters of the models that should not or cannot be individualized can be found.
- the concentration-time profile at the site of action obtained in 2.1 1 is then compared with the target profile obtained in step 1 ( Figure 2, 2.12). If the target concentration-time profile and the actual concentration-time profile at the site of action correspond, or if the difference between the two falls below a threshold that is either predefined or determined from the optimization process, then the dosing scheme used as an input function in 2.9 is recorded as an optimized dosing scheme ( Figure 2, 2.14) and step 2 ( Figure 2, 2.2) and thus the process is ended.
- the deviation of the two can be quantified by a suitable measure. With this measure it can e.g. by a continuous size such as a distance square or e.g. also by a discrete size such as act the number of violations of a criterion.
- an optimization step is carried out ( Figure 2, 2.13) in which the input dosing scheme ( Figure 2, 2.9) is changed.
- All known numerical and analytical optimization methods can be used as methods for carrying out the optimization.
- gradient methods e.g. Newton or Quasi-Newton method
- gradient-free methods e.g. interval nesting
- stochastic methods e.g. Monte Carlo method
- evolutionary methods e.g. genetic optimization
- the particular execution of an analytical optimization process can result from the ADME model type used. All individual steps are repeated iteratively until a match between target effect and actual effect is achieved in 2.6 and step 1 can be canceled in 2.8.
- a variant of the method enables the treatment of several effects (e.g. effect and side effect) that are caused by an active ingredient, a substance at one site of action (Figure 3).
- the effect model in Figure 2.4 is replaced by any number (1 to N) of effect models for this site ( Figure 3, 3.2).
- the effects calculated by these models ( Figures 3, 3.3; 1 to N) are compared with a number of target effects and the entire optimization process is repeated from step 1.
- the method can be carried out both on several active substances and on several active sites with multiple effects and any combination of active ingredients, active sites and effects (Figure 4).
- Several concentration-time profiles at one or more active sites for one or more active ingredients or sub- punch Figure 4, 4.1.
- several target concentration time profiles are calculated in this case ( Figure 4, 4.6).
- a special variant of the method outlined in Figure 4 results from interactions and coupling of the effects of several active substances or several effects at one or more sites (Figure 5).
- the group of effect models in 4.2 must be replaced by an integrated effect model ( Figure 5, 5.2). All other steps remain unchanged.
- the changed requirements for the optimization process ( Figure 5, 5.5) arise naturally. It should be noted that interactions between different substances can also influence their ADME behavior. How to treat such couplings in ADME behavior is described after the variants of the method for the treatment of coupled effects (see below).
- step 1 of the method described in Figures 4, 5 and 6 require variants for step 2 of the method that differ from the one described in Figure 2.
- the ADME models in Figure 8 (8.2) must be replaced by an integrated ADME model ( Figures 9, 9.2).
- the treatment of administration / intake of one or more substances via several application routes e.g. oral, intravenous, intraarterial, intramuscular, dermal, inhalation, topical, treatable.
- ADME models are suitable as ADME models, particularly suitable and preferred according to the invention are the methods for PBPK modeling claimed in DE A 10160270 and DE A 10345836.
- pharmacokinetic variables derived from concentration-time profiles are the target function in step 2 of the process.
- derived pharmacokinetic parameters include e.g. Maximum concentrations, integrals of the concentration-time curves, half-lives, mean dwell times and times of exceeding a threshold.
- the method according to the invention can also be used as an aid in clinical or animal experiments, for example in order to enter the test series with clinically “sensible” dosages and the typical “leveling” of the dosage , that is, to reduce the empirical-iterative approach of too large to too small dosages, which alternately approach the optimum, to a minimum, thus minimizing the burden on the treated organisms and maximizing the probability of the experiment being successful.
- Organism for which the method can be carried out accordingly people, animals, and plants, in particular humans, as well as farm animals, breeding, laboratory, experimental and hobby animals.
- the method is very particularly preferably used as an aid for the therapeutic treatment of humans or clinical trials on humans.
- Livestock and breeding animals include mammals such as Cattle, horses, sheep, pigs, goats, camels, water buffalos, donkeys, rabbits, fallow deer, reindeer, fur animals such as Mink, chinchilla, raccoon, birds such as Chickens, geese, turkeys, ducks, pigeons, bird species for home and zoo keeping.
- Laboratory and experimental animals include mice, rats, guinea pigs, hamsters, rabbits, dogs, cats, pigs and monkeys in all species, subspecies and breeds.
- Hobby animals include dogs, cats, birds and fish.
- a narrow therapeutic window means that there is only a small concentration range in which the desired pharmacological effects occur but at the same time no undesirable side effects can be observed.
- indication areas are all types of cancer, infectious diseases, in particular bacterial and viral infections, cardiovascular diseases, in particular high blood pressure, lipidemia, angina pectoris and heart attack, diseases of the central nervous system such as Alzheimer's disease, schizophrenia, epilepsy, chronic headaches (migraines), painful diseases - Therapy and anesthesia, mental illnesses, especially depression and anxiety, metabolic diseases such as B.
- Diabetes and disorders of fat metabolism such as asthma and bronchitis
- respiratory diseases such as asthma and bronchitis
- immune diseases in particular allergies, rheumatism and multiple sclerosis
- diseases of the gastrointestinal tract in particular ulcers of the stomach and duodenum and Crohn's disease
- diseases of the vessels ins - special ones that lead to erectile dysfunction and acute shock conditions.
- Figure 1 Schematic representation of the general optimization problem for predicting an optimal dosage of active ingredients.
- Figure 2 Schematic representation of the two-step procedure for finding doses and doses.
- Figure 3 Schematic representation of step 1 of the two-step procedure for finding doses and doses for several effects at one site of action.
- Figure 4 Schematic representation of step 1 of the two-step procedure for finding the dose and dosage for several effects and / or active substances and / or active sites.
- Figure 5 Schematic representation of step 1 of the two-step procedure for finding doses and doses for several effects and / or active substances and / or active sites for couplings and interactions between the effects, active substances and active sites.
- Figure 6 Schematic representation of step 1 of the simplified two-step procedure for dose and dose determination in the absence of couplings.
- Figure 7 Schematic representation of step 2 of the procedure for the temporal dosing of drugs for several sites.
- Figure 8 Schematic representation of step 2 of the procedure for the temporal dosing of drugs for several active substances and / or active sites.
- Figure 9 Schematic representation of step 2 of the procedure for the temporal dosing of drugs for several active substances and / or active sites and / or types of application and in the presence of interactions between the ADME behavior.
- Figure 10 Schematic representation of step 2 of the simplified procedure for the temporal dosing of drugs in the absence of couplings and interactions.
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- Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
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- Pharmaceuticals Containing Other Organic And Inorganic Compounds (AREA)
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Abstract
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Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
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JP2007513744A JP2008505377A (ja) | 2004-05-25 | 2005-05-14 | (2段階)投薬及び投薬決定のための方法 |
US11/569,449 US20070253903A1 (en) | 2004-05-25 | 2005-05-14 | Method for (Two-Step) Dosing and Dosage Finding |
EP05750073A EP1759324A2 (de) | 2004-05-25 | 2005-05-14 | Verfahren zur (zweistufigen) dosis- und dosierungsfindung |
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DE102004025534A DE102004025534A1 (de) | 2004-05-25 | 2004-05-25 | Verfahren zur (zweistufigen) Dosis- und Dosierungsfindung |
DE102004025534.2 | 2004-05-25 |
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WO2005116854A2 true WO2005116854A2 (de) | 2005-12-08 |
WO2005116854A3 WO2005116854A3 (de) | 2006-12-28 |
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PCT/EP2005/005315 WO2005116854A2 (de) | 2004-05-25 | 2005-05-14 | Verfahren zur (zweistufigen) dosis- und dosierungsfindung |
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US (1) | US20070253903A1 (de) |
EP (1) | EP1759324A2 (de) |
JP (1) | JP2008505377A (de) |
DE (1) | DE102004025534A1 (de) |
WO (1) | WO2005116854A2 (de) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2009545394A (ja) * | 2006-08-01 | 2009-12-24 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | 生物学に導かれた適応的な治療計画 |
EP2668945A1 (de) | 2012-06-01 | 2013-12-04 | Bayer Technology Services GmbH | Genotyp- bzw. Phänotyp-basierte Arzeimittelformulierungen |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
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DE102005028080A1 (de) * | 2005-06-17 | 2006-12-21 | Bayer Technology Services Gmbh | Verfahren zur zeitlich gesteuerten intravenösen Verabreichung des Narkosemittels Propofol |
DE102006028232A1 (de) * | 2006-06-20 | 2007-12-27 | Bayer Technology Services Gmbh | Vorrichtung und Verfahren zur Berechnung und Bereitstellung einer Medikamentendosis |
US8554712B1 (en) | 2012-12-17 | 2013-10-08 | Arrapoi, Inc. | Simplified method of predicting a time-dependent response of a component of a system to an input into the system |
US10603390B2 (en) | 2013-07-29 | 2020-03-31 | The Regents Of The University Of California | Real-time feedback system control technology platform with dynamically changing stimulations |
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JPH08299706A (ja) * | 1995-05-09 | 1996-11-19 | Fujita Corp | 濁水処理システム |
US20020010550A1 (en) * | 1998-09-14 | 2002-01-24 | George M. Grass | Pharmacokinetic-based drug design tool and method |
US20030087869A1 (en) * | 2001-09-28 | 2003-05-08 | Ebens Allen James | Assay for compounds affecting invertebrate cell secretory pathways |
US20040138826A1 (en) * | 2002-09-06 | 2004-07-15 | Carter Walter Hansbrough | Experimental design and data analytical methods for detecting and characterizing interactions and interaction thresholds on fixed ratio rays of polychemical mixtures and subsets thereof |
EP1546985A4 (de) * | 2002-09-20 | 2010-10-27 | Neurotech Res Pty Ltd | Zustandsanalyse |
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2004
- 2004-05-25 DE DE102004025534A patent/DE102004025534A1/de not_active Withdrawn
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2005
- 2005-05-14 US US11/569,449 patent/US20070253903A1/en not_active Abandoned
- 2005-05-14 WO PCT/EP2005/005315 patent/WO2005116854A2/de active Application Filing
- 2005-05-14 JP JP2007513744A patent/JP2008505377A/ja active Pending
- 2005-05-14 EP EP05750073A patent/EP1759324A2/de not_active Withdrawn
Non-Patent Citations (4)
Title |
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BALANT L P ET AL: "Modelling during drug development" EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS, ELSEVIER SCIENCE PUBLISHERS B.V., AMSTERDAM, NL, Bd. 50, Nr. 1, 3. Juli 2000 (2000-07-03), Seiten 13-26, XP004257177 ISSN: 0939-6411 * |
DERENDORF H ET AL: "Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives." PHARMACEUTICAL RESEARCH. FEB 1999, Bd. 16, Nr. 2, Februar 1999 (1999-02), Seiten 176-185, XP002383973 ISSN: 0724-8741 * |
MEIBOHM B AND DERENDORF H ET AL: "Pharmacokinetics/Pharmacodynamic studies in drug product development" JOURNAL OF PHARMACEUTICAL SCIENCES, AMERICAN PHARMACEUTICAL ASSOCIATION. WASHINGTON, US, Bd. 91, Nr. 1, Januar 2002 (2002-01), Seiten 18-31, XP002989693 ISSN: 0022-3549 * |
WILLMANN S ET AL: "PK-Sim(R): a physiologically based pharmacokinetic 'whole-body' model" BIOSILICO, ELSEVIER, Bd. 1, Nr. 4, 4. September 2003 (2003-09-04), Seiten 121-124, XP004886543 ISSN: 1478-5382 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2009545394A (ja) * | 2006-08-01 | 2009-12-24 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | 生物学に導かれた適応的な治療計画 |
EP2668945A1 (de) | 2012-06-01 | 2013-12-04 | Bayer Technology Services GmbH | Genotyp- bzw. Phänotyp-basierte Arzeimittelformulierungen |
WO2013178565A1 (de) | 2012-06-01 | 2013-12-05 | Bayer Technology Services Gmbh | Genotyp- bzw. phänotyp-basierte arzeimittelformulierungen |
Also Published As
Publication number | Publication date |
---|---|
WO2005116854A3 (de) | 2006-12-28 |
DE102004025534A1 (de) | 2005-12-15 |
US20070253903A1 (en) | 2007-11-01 |
JP2008505377A (ja) | 2008-02-21 |
EP1759324A2 (de) | 2007-03-07 |
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