Molecular optimization of APIs ( DRUG SUBSTANCES)

Molecular optimization of APIs ( DRUG SUBSTANCES)
Introduction:

Drug discovery and development is a complex, lengthy  

process and failure of a candidate molecule at the 

development stage can occur as a result of a combination of 

reasons, such as poor pharmacokinetics, lack of efficacy, 

and/or toxicity.

Improving the pharmacological profile 

of a candidate molecule requires the optimization of 

numerous, often competing objectives (ie, biological or 

chemical properties), to discover the few improved molecules 

that represent the best compromise of the multiple criteria 

important for a successful drug.

Traditionally, when a 

series of compounds with adequate potency had been 

identified and the remaining objectives had to be taken into 

account, the pharmaceutical industry strived to optimize one 

objective at a time – starting with the binding affinity of a 

molecule – as part of a process involving the sequential 

optimization of each biological property, each time followed 

by screening and a large amount of 'tweaking'. This 

approach often results in cycles of trial and error and can be a 

waste of resources and time.

Standard chemoinformatics methods for optimization, which 

are modeled on the traditional experimental optimization 

procedures, ignored the multi-objective nature of the problem 

and focused on the optimization of each single biological or 

chemical property as they became available during the drug discovery 

process.

INTRODUCTION+ The reseach  and drug designing, development and its introduction, is indeed in a big mission. The central activities of pharmaceutical research are drug design and delivery.+ Drug design or discovery of molecular structure which fit certain receptor sites in the protein.+The role of computational modeling in drug design is highly developed, dominated by mature metnodology involving techniques such as quantitative structure acitivity relationships (QSAR), ligand Docking and Molecular Dynamics.

+The old approach has been supplemented by efficient new technologies such as use of gene chips, combinational chemistry, robots that screen more compounds in a month, high speed computers that point to likely drug targets, laser microscope that capture individual cells and X ray crystallography which help to design drug that are receptor specific and thus work with maximum efficiency.

  How  is  this  In Silico  differ  from  mature  methods ?

            1. Despite  tremendous  advancement  in  technology,  it  is  clear  that  molecular variation  of  a  limited number  of  compounds  may  not  deliver  the  desired  drug rather  the  key  lies  in  many  variations  of  many compounds.

            As  for  example,  at  initial  stage  discovery  of  a  “lead”  compound  having some  pharmacological activity  may  be  a  failure  at  the  late  stage  of  development due  to  poor  ADME  characterstics.       

            Failure  of  a  molecule  to  be  drug  at  the  later stage  of  development  could  be  extremely  costly  and loss  of  valuable  time.

            2. The  bottleneck  preventing  this  industry  from  attaining  enhanced  levels  of productivity  includes

* The  time  required  for  and

* Physical  limitations  of  wet  lab  experiments.

            The  In Silico  drug  design  has  generated  much  interest  because  it  can effectively  reduce  the enormous  cost  and  time  required  to  develop  a  new  drug  molecule.

  OBJECTIVE

1. The  objective  of  the  current   research   is  to  build  models  of  metabolic systems  and  simulate  and analyze  them  for  the  rational  design  of  more efficacious  drugs.  The  focus  for  the  research  is  many dangerous  diseases which  is  of  world's  most  significant  health  concerns.

1. This  script  will  focus  on  the  drug  discovery  pipeline  and  how  systems biology  can  play  a  role  in  each of  the  steps,  particularly  in  the  identification  and  validation  of  new  drug  targets.  Target  validation  has traditionally  been  a  laborious  process,  dependent  on  animal  models  and  in vivo  experiments.  Scientist have  developed  Target  on  viruses ,  an  in silico target  validation  tool  for  many bacterial,  viral  based diseases.

  Lead compound.

There  are  two  types,

1. Primary  lead.
2. Secondary  lead.

THE   PRIMARY  LEAD  COMPOUND  is  one,  which  has  been  obtained  without using  extensive information  of  properties  of  any  other  compound.

* Structural  analogues  of  a  lead  is  prepared  by  substitution  of  the  functional  group while  keeping  the  core chemical  structure  more  or  less  same.

* These  analogues  exhibit  varying  degree  of  pharmacological  acivity.

* The  process  of  analogue  generation  to  maximize  the  desire  pharmacological activity  is  known  as  “lead optimization”.  Which   require  the  application  of  quantitative  structure  activity  relationship  (QSAR).  The random  screening  of  vast natural  molecules  in  the  conventional  way  of  obtaining  a  primary  lead  is  time consuming,  costly  and  requires  huge  man  power.

* Most  of  the  existing  drugs  have  been  discovered  by  this  technique.

THE  SECONDARY  LEAD  COMPOUND  is  substantially  different  in  chemical composition  and  structure from  the  primary  lead  compound  and  its  analogues.

1. It may be designed or obtained by utilizing the information related to structural properties  of   the  lead  molecule to  synthesize  the  particular  molecule  having  optimum  pharmacological  properties.
2. As  for  example,

Tolbutamide,  an  oral  hypoglycemic  drug  is  the  offshoot  from Glyprothiazol  which  is  having blood  sugar  lowering  effect.

Obviously,  Tolbutamide  is  pharmacologically  superior  to   the primary lead  Glyprothiazol.

Lead  Optimization.

It  is  the  most  crucial  step  in  drug   design  process  and  involves  synthesis  of  series of  analogues  of  the primary  lead  and  testing  their  pharmacological  and toxicological  activity  to  obtain  better  next  generation  lead molecule.

1. The  underlying  principle  of  lead  molecule  it  that  any  incremental  change  in the  chemical  structure produces  either  positive  or  negative  increment  changes  in  biological  activity.
2. A  systematic  study  of  such  cause  and  effect  relationship  is  called  structure  activity  relationship  (SAR) study.
3. The  present day  “Time to market”  for  a  new  therapeutic  molecule  is considerably  shorter  than  the  past. This   becomes  possible  due  to  application  of  computer  aids  at  various  stages  of  drug  design.
4. Obviously  for  efficient  lead  optimization  computer aids  application  is fast becoming  a necessity. 

Application of Artificial Neural Network (ANN).

* ANN  is  applied  to  correlation  studies  of  the  parameters  of  molecule description  and  is  a  part  of  QSAR.

* ANN   may  be  defined  as  an  attempt  to  mimic  the  way   the  brain  does  things in  order  to  harness  its versatility  and  its  ability  to  infer  and  intuit  the  incomplete  or  confusing  information  without  any apparently  explicit  logical process.

* It  is  superior  to  the  statistical  methods  because  it  does  not  require  to  construct  a  model  which  is often  difficult  in   pharmaceutical  field.

* This  method  is  having  advantages  when  number  of  variable  such  as  molecules  description  are  many and  give  more  statistically  significant  result  with  small  number  of  data  sets.  ANN  fitted  curve  follows the  experimental  data  more closely  compared  to  statistically  fitted  curve.

3-Dimensional  Quantitative  structure  Activity  Relationship. (3D QSAR)

      The  ANN  coupled  with  computer  aided  3 Dimensional  (3D)  molecular modeling  suited  for  QSAR.

Software.

      A  number  of  computer  software  are  available  for  various  stage  of  drug design  as  shown  in  the below.

       

Computer  software  required  in  various  phases  of  drug  Design.

Calculation  of  molecular  description

Structure  determination

Macro molecular  structure  determination.

Data base  handling.

Drug receptor  interaction.  Etc. 

* Soft  ware  names.

1.      GOLEM – For statistical  valildation  of  QSAR  result.2.      PROGOL – More  advanced  than GOLEN,  uses  rational  drescriptors.3.      Beagle -  Helps  to  device  new  discrimination  rules  for testing.

* Hybrid software – These  are  combination  of genetic  algorithm  and  ANN .

    1. GFA – genetic  function  approximation.

    2. EP – Evolutionary  programming.  GFA  and  EP  are  used  for  molecular description  selection  which  have strong  correlation  with  biological   activity.

* 3D-QSAR software.

    1. GOLPE

    2. MUSEUM –   Both  are  used  for  selecting  most  significant  variable  in  3D-QSAR  studies  of  lead optimization.00

Conclusion:

The  computational  model  for  drug  designing  makes  industry’s  R&D  faster  a bit.  This  new role  for  informatics—at  the  core  of  a  new  pharmaceutical  R&D process  focused  on  delivering more  and  better  drugs  in  a  shorter  time  period. Human  ingenuity  should  again  prove  to  be  the pharmaceutical  industry’s ultimate  driver  in  creating  treatment  for  poorly  or  previously  untreated  diseases.