Statistical modeling in pharmaceutical research and development.

Statistical modeling in pharmaceutical research and development. This modelling is used in pharmaceutical industries to overcome the challenges related to pharmaceutical formulation, to reduce cost and increase quality and speed of pharmaceutical products.

1. SUBMITTED BYSUBMITTED BY

2. Statistical modeling in pharmaceutical
research and development
Statistics:

Statistics is a scientific study of numerical data based on natural
phenomena.

It is also the science of collecting, organizing, interpreting and
reporting data.
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reporting data.
Pharmaceutical statistics:

Pharmaceutical statistics is the application of statistics to matters
concerning the pharmaceutical industry.

To compare the action of two or more different drugs or different
dosage of same drug are studied using statistical method.

3. Statistical modeling:

Thenewmajorchallengethatthepharmaceuticalindustryis
facinginthediscoveryand developmentofthenew drugsisto
reducecostsandtimeneededfromdiscoverytomarket, whileat
thesametimeraisingstandardsofquality.

Ifthepharmaceuticalindustrycannotfindasolutiontoreduce
boththecostsandtime,then itswholebusinessmodelwillbe
exposetodangerorrisk.
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exposetodangerorrisk.

Thedevelopmentofthemodelsinthepharmaceuticalindustry
certainlyoneofthesignificant proposedtofacethechallengesof
cost,speedandquality.

Theperspectivetodevelopnewtechnologiesgiverisetohopeof
establishmentnewmodelin thepharmaceuticalindustries
significanttoovercomethechallengesofcost,speedaswellas
quality.

4. Modeling
isnothingbuttheconceptadoptingjustanothernew
technologies.
Theconcept,however,isthatofadoptingjustanothernew
technology,knownasmodeling.Intheuseofstatisticalmodeling
toachievesummaryfromdatatoculturessaidbybreiman.

Thefirstculture
whichisdatamodelingculturewheredata
generatedbyagivenrandom datamodel.
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generatedbyagivenrandom datamodel.

Thesecondculture
whichisthealgorithmicmodeling
culturewhereuseofthealgorithmic model.

5. Objectivesofstatisticalmodeling:

Toimprovethequalityaswellastoreducecostandtime
inexperimentalcycle.

Enhancingthemodelwillhelptogetknowledgethat
experimentonsuccessivedrugaswellasenhancingof
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experimentonsuccessivedrugaswellasenhancingof
modelabilitytoshowrealcondition.

Withoutmodels,thefinalpurposeofanexperimentwas
onesingledrugoritsbehavior.Withtheuseofmodels,
theobjectiveofexperimentwillbethedrugandthe
modelsatthesamelevel.

6. Typeofstatisticalmodeling:
1.
Descriptivemodeling
2.
Mechanisticmodeling
1.
Descriptivemodeling:

Descriptivemodelingisamathematicalprocessthatdescribes
realworldeventsandtherelationshipbetweenfactors
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realworldeventsandtherelationshipbetweenfactors
responsibleforthem.Thegoalofthismodelistoprovide
appropriatedescriptionofdatatounderstandtheconceptof
datageneratingmechanismandthefamilyofmodelisselected
basedtodenotethedatastructure.

Empiricalmodelsarebasedondirectobservation,
measurementandextensivedatarecords.

7. 
Descriptivemodelorempiricalmodel,describesthe
overallbehaviourofthesysteminquestion,without
makinganyclaimaboutthenatureoftheunderlying
mechanismsthatproducethisbehaviour.

Descriptivemodelisagenerictermforactivitiesthat
createmodelsbyobservationand experiment.
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Descriptivemodeloperatesonasimplelogic:the
makerobservesaclose correspondencebetweenthe
behaviourofthemodelandthatofitsreferent
.

8. 2.
Mechanisticmodeling:

Mechanisticmodelsarebasedonthefundamentallawsofnatural
sciences,Physical& biochemicalprinciplesconstitutethemodel
equations.

Whenevertheinterestliesintheunderstandingofthemechanisms
ofaction,itiscriticalto beabletocountonastrong
collaborationbetweenscientists,specialistsinthefield,and
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collaborationbetweenscientists,specialistsinthefield,and
statisticiansormathematicians.

Amechanisticmodelassumesthatacomplexsystemcanbe
understoodbyexaminingthe workingsofitsindividualpartsand
themannerinwhich theyarecoupled.Mechanistic modelstypically
haveatangible,physicalaspect.Inthatsystemcomponentsarereal,
solid andvisible.

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10. Differencebetweendescriptiveandmechanisticmodeling:
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