Patent Publication Number: US-2005137807-A1

Title: Method of visualization of the ADME properties of chemical substances

Description:
The invention relates to a computer system and a method for the visualisation of ADME properties for a multiplicity of chemical substances, and subsequent selection as well as automatic filtering of the substances with the aid of a predetermined requirement profile. This invention is based on an earlier development (DE 101 60 270 A1) and, in relation to it, represents an extension and improvement which greatly simplifies the data evaluation and interpretation.  
      A goal in all fields of chemical research is to synthesize substances which fulfil a particular predetermined requirement profile. Medical active agents, for example, must be capable of reaching the place in the body where they are intended to act (“target”) in order to exhibiting the intended biochemical effect there (for example inhibition of an enzyme, etc.).  
      In order to obtain early information about the likely physical, biological, biochemical, pharmacological or other relevant properties of a substance which has not yet been fully characterized experimentally (and possibly not yet synthesized), structure-property relationships are compiled according to the prior art. Such structure-property relationships are established in many fields of application, such as for the classification of potential active agents in medicinal chemistry or agrochemistry, for assessment of the toxicity of chemical substances, for the early estimation of polymer or catalyst properties, etc.  
      In the field of ADME properties (A=absorption, D=distribution, M=metabolism, E=excretion), which is particularly relevant to pharmaceutical active-agent research, substance properties such as lipophilicity, solubility, permeability across artificial membranes or cell layers, molecular weight and numbers of particular structural features, for example hydrogen donors and acceptors, are usually taken into account. The assessment of the substances then generally involves compliance with particular limits, which are usually obtained from empirical values, expert knowledge or from the statistical distribution of the properties of commercially available products. One extensively used known guideline, which was derived in this way, is Lipinski&#39;s “Rule of Five” for describing orally administered active agents (C. A. Lipinski et al., Adv. Drug Del. Rev. 23, pp. 3-25 (1997)). A crucial disadvantage of such a method (as described in DE 101 60 270 A1), is that they will consider rigid limits for individual properties which are only indirectly relevant. The ADME properties which are actually important, however, generally depend simultaneously on a plurality of these quantities. The tolerable limit for an individual quantity is therefore not in fact a constant, rather it changes its value as a function of the values of other relevant quantities. An improved method, which takes such dependencies into account by the incorporation of complex biophysical models, is described in DE 101 60 270 A1.  
      On the basis of the technique described in DE 101 60 270 A1, the present invention relates to an improved method which, through calculation of the ADME properties for a multiplicity of chemical substances, allows visualization of the properties in the form of so-called ADME maps and subsequent graphical selection and automatic filtering of particularly suitable active-agent candidates with the aid of a predetermined requirement profile, and to a corresponding computer program and method.  
      Visualization of the ADME properties by means of such ADME maps is advantageous compared with a representation of the ADME property in table form (as described in DE 101 60 270 A1), since it compares and contrasts all the substances of the substance library at a glance and therefore allows very straightforward and rapid assessment of the substances in relation to the ADME property.  
      Methods for the visualisation of complex data structures are known per se, and are commercially available in the form of software tools (for example Origin from the OriginLab Corporation or Spotfire). Such pure visualisation tools, however, are configured without any application-specific “intelligence”, i.e. they represent data as it stands but do not per se carry out any interpretation of the information or selection of candidates.  
      The direct linking of a biophysical model with a visualisation tool as described in the present application is novel, as is the combination with application-specific, indication-dependent requirement profiles which relate directly to the ADME properties (and not, as is customary according to the prior art, to the molecular structure of properties). Besides manual selection of particularly suitable active-agent candidates, therefore, automatic filtering and substance assessment may also be carried out. This may either be applied to substance libraries with hundreds of thousands of individual substances, as are nowadays customary in industrial pharmaceutical research, or in the scope of active-agent research projects to assist decision and making project control.  
      The invention relates to a method for the visualization of ADME properties and for the selection of chemical substances and structures with the aid of an indication-specific target profile, with the following steps: 
          a) determining or selecting and subsequently entering molecular properties of a multiplicity of substances or chemical structures into a computer system,     b) setting up one or more ADME maps by means of one or more biophysical models of possible expressions of substance properties for molecules in a selected molecular weight range,     c) linking the chemical structures in a) with the biophysical models in b) and optionally representing the structures as data points in the ADME maps from b) (“mapping”),     d) defining an indication-specific target profile in the ADME property space,     e) classifying the structures with respect to the target profile, for example up to a molecular weight of 1000, and selection with the aid of the classification.        

      The molecular properties according to a) preferably involve a selection from the following properties: 
          lipophilicity, binding constant to plasma proteins, molecular weight, molecular volume, water solubility, solubility in intestinal fluid, permeability coefficient across a biological membrane, fraction unbound in plasma, kinetic constants of a metabolic process, kinetic constants of an active transport process.        

      It is preferable to use, as the biophysical model, one or more respectively selected from the list: 
          physiology-based pharmacokinetic model for mammals     physiology-based pharmacokinetic model for insects     physiology-based pharmacokinetic model for plants.        

      The ADME properties preferably involve a selection of the following: 
          For the case of a model for mammals:     fraction unbound in plasma, organ/blood distribution coefficient, organ/plasma distribution coefficient, distribution volume, terminal half-life in blood, plasma or an organ, intestinal permeability, absorbed fraction of a dose of the substance following oral application, maximum concentration in the blood, plasma or an organ.        

      In the case of a model for plants: 
          characteristic for the rate of absorption into the leaf following a spray application, characteristic for the rate of distribution in the plant following leaf application (phloem mobility), characteristic for the rate of distribution in the plant following root application (xylem mobility).        

      In the case of a model for insects: 
          characteristic for the rate of absorption into an insect through the gut following oral application, characteristic for the rate of absorption into an insect through the cuticle following topical application.        

      In a preferred method, the target profile is obtained from empirical values, expert knowledge and/or the statistical distribution of relevant ADME properties for known substances.  
      The classification is particularly preferably carried out using truth values which represent the fulfilment of an individual requirement of an ADME the property.  
      As an alternative, the classification is particularly preferably performed by combining a plurality of truth values, which represent the fulfilment of an individual requirement, by means of Boolean algebra.  
      In another preferred variant of the method, the classification is performed by means of an index value, which quantifies the deviation from a target value.  
      In another preferred version of the method, the classification is performed by means of a weighted average of a plurality of index values, which quantify the deviation from a target value.  
      Another preferred variant of the method is characterized in that the classification is performed by means of a probability value, which indicates the probability rank in relation to an empirical distribution function obtained from known substances for an ADME property.  
      The input of the substance properties may be performed by importing values from a substance database or by using substance information obtained from experiments, which is available in particular as a file.  
      The selection and filtering may be performed by the user of the computer system using graphical selection, or may be carried out automatically by the computer system using predetermined requirement profiles.  
      Examples of complex biophysical models are physiology-based pharmacokinetic (PBPK) models. Such models are known according to the prior art. A PBPK model for mammals has been mathematically described in detail, for example by Kawai et al. (R. KAWAI, M. LEMAIRE, J.-L. STEIMER, A. BRUELISAUER, W. NIEDERBERGER, M. ROWLAND, “Physiologically Based Pharmacokinetic Study on a Cyclosporin Derivative, SDZ IMM 125”, J  Pharmacokin. Biopharm.  22, 327-365 (1994)). A PBPK model for lepidoptera larvae has been described by Greenwood et al. (R. GREENWOOD, M. G. FORD, E. A. PEACE, D. W. SALT: “The kinetics of Insecticide Action. Part IV: The in vivo Distribution of Pyrethroid Insecticides during Insect Poisoning”  Pestic. Sci.  30, 97-121 (1990)), an example of a PBPK model for plants is the model by Satchivi et al. (Satchivi N. M., Stoller, E. W., Wax L. M., Briskin D. P., A nonlinear dynamic simulation model for xenobiotic transport and whole plant allocation following foliar application Parts I and II. Pest. Biochem. and Physiol. 2000; 68: 67-95).  
      The basic principle is represented in  FIG. 1 . The starting point is a library or database of chemical structures ( 11 ), which contains molecular properties ( 12 ) for a multiplicity of structures. These molecular properties may either have been found experimentally beforehand, or may have been determined with the aid of structure-based prediction methods which are known per se, such as QSAR or neural networks.  
      In a first step, the “ADME map” ( 14 ) is set up for the ADME property of interest. An ADME map is a two-dimensional representation, in particular encoded with false colours or contours, of the ADME property as a function of two or more molecular substance properties due to the structure, on which this ADME property depends. The calculation is preferably carried out—as described in DE 101 60 270 A1—with the aid of biophysical models ( 13 ).  
      The so-called “mapping” is carried out in a second step, i.e. the substances contained in the substance library are represented as data points in this ADME map ( 15 ). The position of any given substance in this ADME map is determined by its respective molecular structure properties. Optionally, additional information may also be represented within an ADME map, for example further molecular structure properties or ADME properties derived from them, the synthesis date, the name of the synthesis chemist etc., for example encoded by colour, symbol or size modulation of the data points. In this way, for example, it is readily possible to reconstruct the historical development of an active-agent research project.  
      The selection of the substances takes place in the third step. A target profile which the substances to be selected should ideally have in relation to the ADME property (or alternatively which they should on no account have) is defined for the selection ( 16 ). In the scope of the invention, the term “indication-specific target profile” is intended to mean selected criteria and values which specify an intended ADME property. The target profile for the ADME property is application-specific. The target profile usually defines a subregion of the ADME map. As such, it may also be highlighted optically, for example by means of bounding lines or by variation of the representation parameters (shade of colour, saturation, etc.) on the colour ADME map. Comparing the position of any given substance on the ADME map with the target profile makes it possible to assess the substances ( 17 ).  
      Steps one to three may be carried out similarly for further relevant ADME properties, so that a substance assessment can be carried out overall on the basis of a plurality of ADME properties.  
      A preferred method for the definition of a target profile is represented in  FIG. 2 . First, a knowledge-based database is prepared about advantageous (and/or particularly disadvantageous) ADME properties ( 24 ). Sources for this knowledge-based database are, for example, empirical values ( 21 ), expert knowledge ( 22 ) and/or similarly to the procedure of C. A. Lipinski et al. [C. A. Lipinski et al.,  Adv. Drug Del. Rev.  23, 3-25 (1997)]—even the statistical distribution of relevant ADME properties for commercially available products (23) (N.B.: but specifically for the ADME property and not just for the molecular structure property!). Suitable sources for such analyses are, for example, databases such as the World Drug Index, the Red List, the Pesticide Manual, the PhysProp database, NCI databases, Medline, etc. The requirements placed on the ADME properties for active agents are generally indication-specific. From this knowledge database ( 25 ), a statistical distribution function for each individual ADME property can then be derived which indicates the probability that a particular ADME property will have a particular value. These probability representations may be employed individually for the classification, or combined to form an individual value (index) by weighted correlation of the individual probability representations ( 26 ).  
      A preferred method for the subsequent assessment of the substances is represented in  FIG. 3 . Each data point is then studied on each ADME map to see whether it belongs to the target profile space. This may, for example, be done in the scope of a qualitative classification in which a check is made to see whether a data point lies inside or outside the target region (yes/no analysis), or a quantitative classification through generation of an index value ( 31 ). In the latter variant, absolute or relative weightings are calculated for each individual requirement (for example based on the distance of a data point from the boundary line of the target profile, or as a probability value which is derived from the empirical distributions for known commercially available substances). The weighted sum of the individual classifiers may be calculated in order to form a overall index value ( 32 ). This overall index value determines the ranking of the substances ( 33 ). The result, which represents a subset of the original substances ( 34 ), may be output as a table or in the form of graphs ( 35 ).  
    
    
     Examples  
      The subsequent examples of the present invention are based on the following biophysical model: The ADME maps in  FIGS. 4 and 9  for the maximum absorbed fraction of an orally administered dose and  FIG. 10  for the fraction dose absorbed, on the basis of a continuous model for gastrointestinal flux and absorption of an oral dose. This model combines physiological influencing factors, such as geometrical dimensions, pH profile and effective surface area of the gastrointestinal tract, with a physiological flux profile described via an intestinal transit function (T si (z,t)) and two substance-dependent parameters: the intestinal permeability (P int ) and the intestinal solubility (S int ). The relevant physiological parameters are represented summarily in  FIG. 12 .  
      The transit profile defines the fraction of an orally administered dose at a position z in the small intestine (z=0 defines the pylorus) at a time t (after oral administration of the substance). Based on an experimental data record by Sawamoto et al. (T. Sawamoto, S. Haruta, Y. Kurosaki, K. Higaki and T. Kimura. Prediction of the Plasma Concentration Profiles of Orally Administered Drugs in Rats on the basis of Gastrointestinal Transit Kinetics and Absorbability,  J. Pharm. Pharmacol.  49: 450-457 (1997)), it was approximated by a Gaussian function with time-variable centroid z o (t) and width σ(t):  
                 T     S   ⁢           ⁢   I       ⁡     (     z   ,   t     )       =         1   -     exp   ⁢           ⁢     {       -   t     /     τ   GE       }               2   ⁢           ⁢   π       ⁢           ⁢     σ   ⁡     (   t   )           ⁢           ⁢   exp   ⁢     {     -         (     z   -       z   o     ⁡     (   t   )         )     2       2   ⁢           ⁢       σ   2     ⁡     (   t   )             }               (   1   )             
 
      Here, τ GE  denotes the time constant for release of the substance from the stomach into the intestine, which was assumed to be 30 min in the model. The time-variable parameters z 0 (t) and σ(t) are approximated by an exponential function and a ninth-order polynomial  
                       z   o     ⁡     (   t   )       =     α   +       β   ⁡     (     t   -     t   0       )       n             ⋀           σ   ⁡     (   t   )       =       ∑     k   =   0     9     ⁢       γ   k     ⁢           ⁢     t   k                       (   2   )             
 
      with the coefficients:  
                                                   Model parameter   Value                                                    α   −6.1           B   10.43           t 0     0.07           n   0.081           γ 0     0.32191           γ 1     2.86798           γ 2     −6.89234           γ 3     8.01795           γ 4     −5.19735           γ 5     2.04239           γ 6     −0.50334           γ 7     0.07631           γ 8     −0.0065           γ 9     0.000237493                      
 
      The concentration of the substance at the position z in the intestinal lumen at time t can be calculated from this as follows:  
                 C   lumen     ⁡     (     z   ,   t     )       =         DOSE   ⁢           ⁢   BW   ⁢           ⁢     (     1   -       f   abs     ⁡     (   t   )         )         π   ⁢           ⁢       r   2     ⁡     (   z   )       ⁢           ⁢     L   SI         ⁢       T   SI     ⁡     (     z   ,   t     )                 (   3   )             
 
      Here, DOSE denotes the administered dose, BW stands for the body weight, L S1  is the total length of the intestine (=280 cm), f abs (t) is the fraction already absorbed at time t. The solubility may limit the amount absorbed, since the substance precipitates in the gastrointestinal tract if luminal concentrations locally occur which exceed the value of the solubility (S int ). This case is taken into account by a threshold condition, which always limits the luminal concentration to the value of the intestinal solubility:  
               C   lumen     =     {             C   lumen     ,             if   ⁢           ⁢     C   lumen       ≤     S   int                   S   int     ,             if   ⁢           ⁢     C   lumen       &gt;     S   int                       (   4   )             
 
      Overall, the amount of substance amount of substance which is absorbed across the intestinal membrane into the portal vein in the region [z. . . . z+dz] in the time interval [t . . . t+dt] is therefore obtained as:  
                   ⅆ   2     ⁢       M   pv     ⁡     (     z   ,   t     )             ⅆ   z     ⁢           ⁢     ⅆ   t         =       P   int     ⁢           ⁢     C   lumen     ⁢           ⁢     (     z   ,   t     )     ⁢           ⁢       ⅆ       A   eff     ⁡     (   z   )           ⅆ   z                 (   5   )             
 
      Numerical integration of this differential equation with respect to positions provides the absorption profile of the substance as a function of time, and integration with respect to time provides the amount absorbed overall in a segment of the gastrointestinal tract. The fraction absorbed overall (Fraction Dose Absorbed) is given by:  
               F   abs     =       ∫     t   =   0     ∞     ⁢       ∫     z   =   0       L   SI       ⁢           ⅆ   2     ⁢       M   pv     ⁡     (     z   ,   t     )             ⅆ   z     ⁢           ⁢     ⅆ   t         ⁢     ⅆ   z     ⁢           ⁢       ⅆ   t     /     (     DOSE   ⁢           ⁢   BW     )                     (   6   )             
 
      With the assumption that the solubility does not have any limiting influence (i.e. C lumen &lt;S int  is satisfied at all times for any position), the maximum absorbed fraction of an orally administered dose which is represented in  FIGS. 4 and 9  is obtained.  FIG. 10  shows the general case with solubility limitation.  
      The intestinal permeability is therefore the only quantity which determines the maximum absorbed fraction of an orally administered dose. Between this quantity and the physicochemical substance parameters of lipophilicity (MA) and molecular weight (MW), there is a biophysical relationship which is given by the following equation:  
                 P   int     ⁡     (     MW   ,   MA     )       =       A   ⁢           ⁢         MW       -   α     -   β       ⁢   MA         MW     -   α       +     B   ⁢           ⁢   M   ⁢           ⁢     W     -   β       ⁢   MA           +     C   ⁢           ⁢         MW     -   γ           D     -   γ       +     MW     -   γ           ⁢           [     cm   ⁢     /     ⁢   s     ]                 (   7   )             
 
      The parameters A, B, C, D, α, β and γ have the values:  
                                                       A   B   C   D   α   β   γ                  7440   1.0 × 10 7     2.5 × 10 −7     202   0.60   4.395   16                  
 
      The first example shows an ADME map for the maximum absorbed fraction of an orally administered dose in humans, which was calculated according to the method described above with the aid of a physiology-based pharmacokinetic model. In addition, two selection criteria known according to the prior art for oral active agents, which belong to Lipinski&#39;s “Rule of Five”, are also shown as lines (lipophilicity &lt;5 and molecular weight &lt;500).  
      According to the Lipinski rules, for example, active agents are unsuitable for passive absorption following oral administration if they have a lipophilicity &gt;5 and a molecular weight &gt;500 (identified by (−/−) in  FIG. 4 .). The complex biophysical model, however, takes into account the combined influence of these two parameters on the oral administration. Accordingly, under particular circumstances (sufficient solubility), even a substance with a molecular weight &gt;500 and a lipophilicity &gt;5 is capable of permeating the intestinal membrane and therefore being orally absorbed. Examples of such substances, which can be passively absorbed well in spite of high lipophilicity and high molecular weight, are itraconazoles (De Beule K., Van Gestel J., Drugs. 2001; 61 Suppl. 1: pp. 27-37), paclitaxel [Walle and Walle,  Drug Metab Dispos.  1998 Apr. 26(4): 343-6] or cyclosporins [Fricker et al., Br. J. Pharmacol. 1996 Aug. 0.118 (7): 1841-7]. Conversely, there are known passively absorbed substances with a low lipophilicity and a low molecular weight which may be expected to have good absorption according to the Lipinski rules (i.e. they are inter alia layer in the region (+/+) in  FIG. 4 ), but which are nevertheless orally absorbed only weakly. One such example is the substance ganciclovir [Wessel et al., J. Chem. Inf. Comput. Sci. 1998, 38, 726-735]. The biophysical model, however, correctly predicts a fraction dose absorbed of less than 8% for this substance. These examples illustrate the superiority of the biophysical model approach over the statistical selection criteria for simple physicochemical parameters.  
      The second example shows a selection of ADME maps for a data record of commercially available substances with various indication fields. The following measurement values were experimentally collected for the substances contained in this data record: membrane affinity as a measure of the lipophilicity (LogMA), binding constant to human serum albumin (LogHSA), both based on the TRANSIL® technology developed by Nimbus, Leipzig. The effective molecular weight (MW) is obtained simply from the respective empirical formula of the substance. The waters solubilities and the typical administered dosages of these commercially available products are furthermore known from the literature.  
      The ADME maps in FIGS.  5  to  10  show by way of example a selection of commercially available pharmaceutical substances. The substance names and the associated experimental measurement values for their physicochemical properties are summarised in Table 1.  
      The data points in the ADME maps of  FIG. 11  represent a selection of agrochemical active agents, the relevant physicochemical parameters of which are listed in Table 2.  
      The organ-blood distribution coefficients for the various organs in FIGS.  5  to  8  were found according to the method described in DE0010160270 (page 5 starting at paragraph [0051] by using the data in  FIG. 3 ).  
       FIG. 5  shows by way of example the map for the fat/plasma distribution coefficient, which was found according to the method described in DE 101 60 270 A1.  
       FIG. 6  shows by way of example the map for the human distribution volume, which was found according to the method described in DE 101 60 270 A1.  
       FIG. 7  shows by way of example the map for the fraction unbound in plasma, which was found according to the method described in DE 101 60 270 A1.  
       FIG. 8  shows by way of example the map for the intestinal permeability coefficient, which was found according to the method described in DE 101 60 270 A1.  
       FIG. 9  shows by way of example the map for the maximum absorbed dose in humans in the permeation-limited case, which was found according to the described method with the aid of a physiology-based pharmacokinetic model.  
       FIG. 10  shows by way of example the map for the absorbed dose in humans in the permeation- or solubility-limited case, which was found according to the method described in DE 101 60 270 A1 with the aid of a physiology-based pharmacokinetic model.  
      The ADME map for the phloem mobility in  FIG. 11  was found with a PBPK model for plants, which is fully described in Satchivi et al. (Satchivi N. M., Stoller, E. W., Wax L. M., Briskin D. P., A nonlinear dynamic simulation model for xenobiotic transport and whole plant allocation following foliar application Parts I and II. Pest. Biochem. and Physiol. 2000; 68: 67-95).  
      Such ADME maps can be used particularly well in a research project, in order to obtain an intuitive graphical overview of the ADME properties of a library of substances. The ranking is carried out in combination with indication-specific rules. Such indication-specific rules may, for example, define a threshold value for the fraction unbound in plasma, a limit value for the fat/plasma distribution coefficient, a threshold value for the distribution volume or the fraction of the orally absorbed dose. In the physicochemical parameter space, such limit values for ADME properties represent nonlinearly bounded regions which result from the underlying biophysical models (see  FIGS. 5-10 ). The preferential region may be highlighted in colour (for example by modulating the colour saturation). Substances which fulfil the requirement profile may then easily be selected and highlighted. When a plurality of such preferential regions are combined by means of Boolean algebra, a classification of the substances may be made in relation to the preferred ADME profile. Further information may be visualised by colour and/or size modulation of the data points.  
      The use of the described technique is not restricted to applications in the field of pharmaceutical research, from which the examples described above come. Utilisation is also possible in other fields for which ADME properties of substances are important, and where biophysical models are available for calculating them. One example is the distribution of crop protection agents or other substances in plants. Owing to the large pH differences inside the plant, the transport in the plant depends not only on the lipophilicity of the substances but also strongly on their pKa values. One important property is the distribution of substances from treated leaves into other parts of the plant (the so-called phloem mobility).  FIG. 11  shows a corresponding contour-encoded property map, in which regions of strong translocation (contour values &gt;10 −1 ) and weak translocation (contour values &lt;10 −3 ) can be seen. These property maps were set up by means of the described physiology-based plant model. It can be seen clearly that, here again, classification of the indicated data points is not possible with simple rules, which rely on the values of lipophilicity and pKa, whereas substances with a particular distribution behaviour can be readily identified according to the method described above.  
               TABLE 1                          Compound list and parameters                                                         Solubility   Typical Mass or           #   COMPOUND   MW   LogMA   [mg/L]   Dose (p.o.)   LogHSA                                                     1   Acebutolol   336   1.792   259   300   mg   −2.301       2   Acetylsalicylic acid   180   0.301   4600   1200   mg   −3.097       3   Acyclovir   225   −0.097   33990   100-600   mg   −2.000       4   Alprenolol   249   2.699   547   100   mg   —       5   Amiloride   214   0.301   1256   20   mg   —       6   Amlodipine   377   4.477   60   5-10   mg   −5.000       7   Amoxicilin   365   0.778   3433   375-1000   mg   −2.301       8   Ampicillin   349   1.114   3574   500   mg   −4.119       9   Antipyrine   188   1.146   23760   10   mg/kg   −2.161       10   Atenolol   266   0.602   685   200   mg   −2.000       11   Betaxolol   307   2.398   451   20   mg   −3.187       12   Bumetanide   364   2.279   32   0.5   mg   −5.076       13   Captopril   217   0.477   6857   100   mg   −3.222       14   Carbamazepine   236   2.519   17.7   800-1200   mg   −2.539       15   Cefadroxil   363   1.079   1110   500   mg   —       16   Cephalexine   347   0.778   1789   500   mg   −2.357                                         17   Cefazolin   455   0.903   214   —   −3.456       18   Cefmetazole   472   1.079   &gt;500   —   —       19   Cefoperazone   646   1.362   64.2   —   —       20   Cefoxitin   427   1.000   105   —   —       21   Ceftazidime   505   1.000   &gt;500   —   −2.495                                             22   Ceftriaxone   512   0.903   958   400   mg   −3.585       23   Cefuroxime   381   0.477   145   500   mg   −2.553       24   Chloramphenicol   287   2.301   389   250   mg   −2.824       25   Chlorpromazine   301   4.075   2.55   50-100   mg   −3.284       26   Cimetidine   252   1.176   10460   200   mg   −1.876       27   Ciprofloxacin   315   0.954   11480   200   mg   −2.675       28   Clomipramin   297   4.104   0.294   50   mg   −2.796       29   Clonidine   194   1.602   13580   0.3   mg   −2.463       30   Clozapine   309   3.951   11.8   300-600   mg   −3.814       31   Caffeine   194   0.602   2632   1-300   mg   −2.222       32   Corticosterone   346   2.531   143   1-5   mg   −3.886                                         33   Coumarin   146   1.505   1900   —   −3.584                                             34   Despiramine   266   3.725   0.99   50   mg   −3.398       35   Dexamethasone   392   2.833   93   1.5   mg   −3.114       36   Diazepam   267   3.170   59   10-20   mg   −4.010       37   Diclofenac   260   2.940   5.61   50   mg   −6.000       38   Dicloxacillin   434   2.079   3.63   250-500   mg   −4.064       39   Digoxin   391   1.477   64.8   1.2   mg   −2.284       40   Diltiazem   415   2.544   &gt;1000   180   mg   −4.031       41   Doxorubicin   544   2.322   93   50-60   mg   −1.886       42   Enalapril   376   1.000   35   10   mg   −2.959       43   Enalaprilate   348   0.176   11   10   mg   —       44   Enoxacin   304   0.954   34300   200   mg   −2.278       45   Etoposide   589   2.000   59   10-600   mg   −4.699       46   Felodipine   348   5.301   20   27.5   mg   −5.301       47   Fleroxacin   321   1.301   7320   400   mg   −2.523       48   Fluconazole   274   1.176   1086   50-150   mg   −2.357       49   Flunitrazepam   354   2.477   72.8   1   mg   −3.046       50   Fluoxetin   261   3.049   2500   30   mg   −4.000       51   Fluvastatin   395   2.146   0.47   2-10   mg   −5.678       52   Furosemide   315   1.342   149   40   mg   −2.469       53   Ganciclovir   255   −0.301   28340   50-100   mg   −1.000                                         54   Gentamicin   478   0.000   1000000   —   −2.469                                             55   Glibenclamid   476   2.301   35   1.25-5   mg   −6.221       56   Guanabenz   209   0.602   1055   16-32   mg   −3.745       57   Haloperidol   360   3.723   14   20   mg   −2.745       58   Hydrochlorothiazide   280   1.146   1292   12.5-75   mg   −3.222       59   Hydrocortisone   433   2.716   219   200   mg   −4.398       60   Ibuprofen   192   1.778   2440   400   mg   −5.301       61   Imipramine   280   3.190   1   40-60   mg   −3.301       62   Indomethacin   340   2.255   3.11   50   mg   −4.260       63   Isradipin   371   3.699   49   5-20   mg   −4.301       64   Ketoprofen   254   1.505   120   25-200   mg   −5.056       65   Labetalol   328   3.620   73   600   mg   −3.770                                         66   Lidocaine   319   1.771   4100   —   −3.301                                             67   Lisinopril   406   0.845   13   10-20   mg   −2.215       68   Methylprednisolone   374   2.000   123   0.6   mg/kg   −3.000       69   Metolazone   348   1.531   133   2.5   mg   −4.201       70   Metoprolol   267   1.591   4777   300   mg   −2.000       71   Metotrexate   454   1.176   26000   0.1-10   mg   −3.796       72   N-Acetylprocainamide   277   1.176   &gt;1000   0.5-2.5   mg   −2.268       73   Nadolol   309   0.602   22400   80   mg   —       74   Naproxen   230   1.653   145   250   mg   −5.208       75   Nicardipine   480   4.646       20-30   mg   −5.000       76   Nifedipine   346   3.778   56.3   30-60   mg   −4.912       77   Nimodipine   419   4.279   24.3   30   mg   −5.000       78   Nisoldipine   388   4.903   25   10-20   mg   −5.319       79   Nitrendipine   360   4.477   77   20   mg   −4.824       80   Nordiazepam   253   3.086   57   10   mg   −4.097       81   Norfloxacin   303   0.602   177900   400   mg   −3.000       82   Ondansetron   293   2.672   5.7   8   mg   −3.237       83   Oxacepam   313   2.342   179   15   mg   −4.000       84   Oxprenolol   265   2.301   3182   160   mg   −3.301       85   Paracetamol   151   0.778   30350   500-1000   mg   −2.000       86   Pefloxacin   315   1.519   11400   400-600   mg   −2.469       87   Penicilin G   334   1.041   50   500   mg   −4.301       88   Pentazocin   285   2.633       50   mg   −2.510       89   Pentobarbital   226   2.176   679   50-100   mg   −2.523       90   Phenobarbital   232   0.778   1110   30-200   mg   −2.854       91   Phenoxymethyl penicillinic acid   350   1.146   101   22   mg   −3.102       92   Phenylbutazone   308   1.845   47.5   300   mg   −4.839       93   Phenytoin   252   2.778   1267   400   mg   −3.639       94   Pindolol   248   2.398   7883   15   mg/kg   −2.699       95   Piroxicam   331   2.079   521   20   mg   −5.301       96   Practolol   252   1.301   4472   25-600   mg   —       97   Prazosin   383   2.544   310   1   mg   −4.141       98   Prednisolone   360   2.568   221   10-50   mg   −3.694       99   Probenecid   285   1.322   27.1   500   mg   −4.004       100   Procainamide   235   1.176   9450   4.5   mg   −2.301       101   Progesteron   314   3.843   5   1-2.5   mg   −6.189       102   Promazine   284   3.823   14.2   100   mg   −3.301       103   Promethazine   284   3.992   15.6   25-200   mg   −3.028       104   Propranolol   259   3.146   609   300   mg   −4.028       105   Quinidine   324   2.699   104   330   mg   −3.539       106   Ranitidine   272   1.079   24660   40-80   mg   −2.155       107   Salicylic acid   138   0.602   3808   100-500   mg   −3.523                                         108   Scopolamine   303   1.462   17400   —   −2.301                                             109   Sotalol   309   1.301   136800   240   mg   −1.678       110   Sulfamethizole   270   1.000   1050   500-1000   mg   −4.000       111   Sulfasalacine   398   2.748   2.44   2000   mg   −5.469       112   Sulpiride   341   0.778   2275   200   mg   −2.933       113   Tenidap   303   1.699   2676   120   mg   —       114   Terazosin   424   1.785   205   7.5   mg   −4.000       115   Terbutaline   211   0.845   212800   5   mg   −2.398       116   Testosteron   180   2.826   68   20   mg   −4.912       117   Tetracycline   444   1.699   231   500-1000   mg   −2.523       118   Theophiline   180   0.886   2800   400-800   mg   —       119   Timolol   316   1.477   2741   30   mg   −2.000       120   Tolbutamid   270   1.415   109   1000   mg   −4.211       121   Tranexamic acid   157   −0.301   25000   500-2000   mg   −4.000       122   Trihexylphenidyl   302   2.415   10000   1-4   mg   −3.006       123   Valproic acid   144   1.041   895   600   mg   −3.921       124   Verapamil   455   3.425   4.47   80-160   mg   −3.155       125   Warfarin   308   1.505   17   5   mg   −4.492       126   Zidovudine   267   0.903   311   10   mg/kg   −3.000                  
 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                   
               
               
                 Crop Protection Example 
               
            
           
           
               
               
               
               
            
               
                   
                 Name 
                 logP 
                 pKa 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 metsulfuron 
                 2 
                 3.3 
               
               
                   
                 metsulfuron-methyl 
                 1.2 
                 3.3 
               
               
                   
                 halosulfuron 
                 −0.0186 
                 3.44 
               
               
                   
                 halosulfuron-methyl 
                 2.9 
                 3.44 
               
               
                   
                 primisulfuron 
                 0.06 
                 3.47 
               
               
                   
                 amidosulfuron 
                 1.63 
                 3.58 
               
               
                   
                 azimsulfuron 
                 2.1 
                 3.6 
               
               
                   
                 chlorsulfuron 
                 1.9 
                 3.6 
               
               
                   
                 prosulfuron 
                 2.8 
                 3.76 
               
               
                   
                 imazosulfuron 
                 2.7 
                 4 
               
               
                   
                 rimsulfuron 
                 1.3 
                 4 
               
               
                   
                 thifensulfuron 
                 0.02 
                 4 
               
               
                   
                 thifensulfuron-methyl 
                 1.23 
                 4 
               
               
                   
                 chlorimuron 
                 0.11 
                 4.2 
               
               
                   
                 chlorimuron-ethyl 
                 2.7 
                 4.2 
               
               
                   
                 triflusulfuron-methyl 
                 3.4 
                 4.4 
               
               
                   
                 nicosulfuron 
                 0.5 
                 4.6 
               
               
                   
                 triasulfuron 
                 1.6 
                 4.64 
               
               
                   
                 flupyrsulfuron-methyl- 
                 1.3 
                 4.9 
               
               
                   
                 sodium 
               
               
                   
                 tribenuron 
                 −0.44 
                 5 
               
               
                   
                 tribenuron-methyl 
                 1.5 
                 5 
               
               
                   
                 oxasulfuron 
                 1.1 
                 5.1 
               
               
                   
                 bensulfuron 
                 0.62 
                 5.2 
               
               
                   
                 bensulfuron-methyl 
                 2.45 
                 5.2 
               
               
                   
                 sulfometuron 
                 −0.51 
                 5.2 
               
               
                   
                 sulfometuron-methyl 
                 1.4 
                 5.2 
               
               
                   
                 diclofop 
                 4.5 
                 3.43 
               
               
                   
                 2,4-D 
                 2.58 
                 2.73 
               
               
                   
                 MCPA 
                 2.75 
                 3.07 
               
               
                   
                 dichlorprop-P 
                 2.58 
                 3.67 
               
               
                   
                 dicamba 
                 2.8 
                 1.97 
               
               
                   
                 bifenox 
                 4.5 
                 13.8 
               
               
                   
                 quinclorac 
                 3.6 
                 4.34 
               
               
                   
                 metosulam 
                 2.5 
                 4.8 
               
               
                   
                 bromacil 
                 1.88 
                 9.27 
               
               
                   
                 diuron 
                 2.85 
                 13.8 
               
               
                   
                 monolinuron 
                 2.2 
                 13.8 
               
               
                   
                   
               
            
           
         
       
     
      The foregoing is only a description of a non-limiting number of embodiments of the present invention. It is intended that the scope of the present invention extend to the full scope of the appended issued claims and their equivalents.