Streamlining Stability Analysis: Tools and Techniques for Efficient Shelf-life Estimation in the Pharmaceutical Industry

Sta­bi­li­ty test­ing, also known as shelf-life assess­ment, stand as a pivo­tal stage in the phar­maceu­ti­cal deve­lo­p­ment, cru­cial for veri­fy­ing the iden­ti­ty, poten­cy and puri­ty of ingre­di­ents within a for­mu­la­ti­on.

Phar­maceu­ti­cal sta­bi­li­ty is defi­ned as the abili­ty of a spe­ci­fic for­mu­la­ti­on within its desi­gna­ted con­tai­ner to main­tain con­for­mi­ty to its phy­si­cal and che­mi­cal pro­tec­ti­ve stan­dards. In other words, it deno­tes the product’s capa­ci­ty to retain its initi­al pro­per­ties and attri­bu­tes throug­hout the sto­rage within defi­ned para­me­ters. Sta­bi­li­ty test­ing ser­ves as a com­pre­hen­si­ve assess­ment of the impact of envi­ron­men­tal varia­bles on drug sub­s­tances or for­mu­la­ted pro­ducts, faci­li­ta­ting the pre­dic­tion of shelf life, iden­ti­fi­ca­ti­on of opti­mal sto­rage con­di­ti­ons, and for­mu­la­ti­on of pre­cise label­ling ins­truc­tions. The data deri­ved from sta­bi­li­ty test­ing is an important requi­re­ment for regu­la­to­ry appr­oval of drugs and for­mu­la­ti­ons.

To deter­mi­ne the dura­ti­on for which a phar­maceu­ti­cal pro­duct can be safe­ly stored wit­hout any chan­ge in its ori­gi­nal che­mi­cal com­po­si­ti­on, adhe­rence to the gui­de­lines set forth by the Inter­na­tio­nal Coun­cil for Har­mo­ni­sa­ti­on (ICH) is impe­ra­ti­ve. The ICH gui­de­line for Sta­bi­li­ty Test­ing of New Drug Sub­s­tances and Pro­ducts Q1A(R2) out­lines pro­ce­du­res for sta­bi­li­ty test­ing of new pro­ducts inten­ded for human use. Addres­sing both inter­me­dia­te and long-term sto­rage con­di­ti­ons, the gui­de­line estab­lishes robust sta­bi­li­ty stu­dy pro­to­cols. The­se pro­to­cols man­da­te the com­pre­hen­si­ve assess­ment of phy­si­cal, che­mi­cal, bio­lo­gi­cal, and micro­bio­lo­gi­cal attri­bu­tes uti­li­zing vali­da­ted sta­bi­li­ty-indi­ca­ting methods.

A relia­ble method to esti­ma­te shelf life invol­ves ana­ly­sing a quan­ti­ta­ti­ve attri­bu­te, such as the degra­da­ti­on of pro­ducts. Accor­ding to the ICH gui­de­lines, we can figu­re this out by fin­ding the ear­liest time when we’re 95% sure that the avera­ge amount of break­down hits a cer­tain level. To do this, we need to test at least three bat­ches of the pro­duct.

When hand­ling mul­ti­ple bat­ches, it is requi­red to pool their data if batch-to-batch varia­ti­on is small. This allows for deter­mi­na­ti­on of an over­all shelf-life esti­ma­te. Howe­ver, befo­re poo­ling the data, it’s cru­cial to assess the batch simi­la­ri­ty. We use a sta­tis­ti­cal test whe­re if the chan­ce of them being dif­fe­rent is less than 25%, we con­sider them simi­lar enough.

The shelf lives for indi­vi­du­al bat­ches can be esti­ma­ted also by using indi­vi­du­al inter­cepts, indi­vi­du­al slo­pes and the poo­led mean squa­re error deri­ved from all bat­ches. Sub­se­quent­ly, the shor­test esti­ma­te among the bat­ches is sel­ec­ted as the retest peri­od or shelf life for all bat­ches.

Figu­re 1: Exam­p­le Shelf-Life Plot. Inter­sec­tion of the blue and green lines show the shelf life of this batch at around 53 months.

Shelf-life esti­ma­ti­on reli­es on the uti­li­sa­ti­on of line­ar regres­si­on models. The flow­chart (Fig. 2) demons­tra­tes the pro­cess of deter­mi­ning the sui­ta­ble regres­si­on model. For ins­tance, if the batch size is three or more, cova­ri­ance ana­ly­sis is per­for­med on the data first to check for inter­ac­tion effects bet­ween batch and time. A sta­tis­ti­cal­ly signi­fi­cant inter­ac­tion sug­gests that the model has com­mon slo­pes, mea­ning that the loss rate of the acti­ve ingre­di­ent is con­sis­tent across all bat­ches. After deter­mi­ning the appro­pria­te model, we can find out the shelf life by loo­king at the time at which the 95% boun­da­ry of the model meets the accep­tance cri­ter­ion.

Figu­re 2: Flow chart for choo­sing the right model

Chal­lenges in Sta­bi­li­ty Test­ing

The sta­bi­li­ty test­ing of phar­maceu­ti­cal pro­ducts typi­cal­ly ent­ails a high­ly intri­ca­te series of pro­ce­du­res, deman­ding con­sidera­ble time and finan­cial resour­ces. Addi­tio­nal­ly, achie­ving an accu­ra­te depic­tion of a drug or product’s shelf life neces­si­ta­tes a pro­found under­stan­ding of sta­tis­tics.

Inno­va­ti­ve ‘Off-the-Shelf’ Solu­ti­ons

Statistica’s Sta­bi­li­ty ana­ly­sis exten­si­on node and Alte­ryx Designer’s sta­bi­li­ty ana­ly­sis tool deve­lo­ped by Stat­Soft enables users to effort­less­ly con­duct sta­bi­li­ty ana­ly­ses. The­se sophisti­ca­ted tools gua­ran­tee robust and accu­ra­te results that com­ply with estab­lished gui­de­lines.

In Sta­tis­ti­ca, for exam­p­le, once you choo­se the rele­vant varia­bles, exe­cu­ting the Sta­bi­li­ty node initia­tes a series of tests, cul­mi­na­ting in a com­pre­hen­si­ve report, pro­vi­ding all essen­ti­al ele­ments for tho­rough sta­bi­li­ty test­ing.

Figu­re 3: Exam­p­le Sta­bi­li­ty Workspace and input Set­tings in Sta­tis­ti­ca

Alte­ryx pro­vi­des users with three tail­o­red ver­si­ons of sta­bi­li­ty stu­dies to suit dif­fe­rent needs:

  1. The app ver­si­on offers a user-fri­end­ly para­me­ter input wit­hout laun­ching Desi­gner, the desk­top appli­ca­ti­on of Alte­ryx. Users sim­ply click on the app to input the data­set and its para­me­ters. A com­pre­hen­si­ve report is expor­ted to a desi­gna­ted fol­der.
  2. The short light­weight Desi­gner ver­si­on stream­li­nes the pro­cess within Alte­ryx Desi­gner, fea­turing a com­pact work­flow. Results are con­ve­ni­ent­ly out­put­ted within Desi­gner and saved in a desi­gna­ted fol­der.
  3. For Alte­ryx enthu­si­asts, the exten­si­ve Desi­gner ver­si­on offers advan­ced opti­ons to tweak para­me­ters, faci­li­ta­ting fle­xi­ble work­flow adapt­a­ti­on. Results are again acces­si­ble within Desi­gner and stored in a local fol­der.
Figu­re 4: Snip­pet of the results report from the Alte­ryx sta­bi­li­ty tool

The­se tools are espe­ci­al­ly advan­ta­ge­ous as they stream­li­ne the enti­re sta­bi­li­ty test­ing pro­cess, saving valuable time and resour­ces for com­pa­nies. By auto­ma­ting the cal­cu­la­ti­ons and pro­ce­du­res invol­ved in sta­bi­li­ty test­ing, inclu­ding data assump­ti­on checks and prepro­ces­sing, our tools eli­mi­na­te the need for manu­al data ent­ry and com­plex cal­cu­la­ti­ons, redu­cing the poten­ti­al for human error and ensu­ring accu­ra­cy and con­sis­ten­cy in results.

Moreo­ver, our tools offer a user-fri­end­ly inter­face that requi­res mini­mal trai­ning to ope­ra­te, making them acces­si­ble to a wide ran­ge of pro­fes­sio­nals within the indus­try.

Fur­ther­mo­re, the effi­ci­en­cy gai­ned through the­se tools allows com­pa­nies to acce­le­ra­te their pro­duct deve­lo­p­ment time­lines, brin­ging new pro­ducts to the mar­ket fas­ter while ensu­ring com­pli­ance with regu­la­to­ry stan­dards. This not only impro­ves ope­ra­tio­nal effi­ci­en­cy but also enhan­ces com­pe­ti­ti­ve­ness in the mar­ket­place.

In addi­ti­on to time savings, our tools pro­vi­de com­pre­hen­si­ve report­ing fea­tures that enable users to gene­ra­te detail­ed ana­ly­sis and insights from sta­bi­li­ty test­ing data. This faci­li­ta­tes infor­med decis­i­on-making and enables proac­ti­ve mea­su­res to be taken to opti­mi­ze pro­duct qua­li­ty and shelf life.

In clo­sing, the jour­ney towards ensu­ring phar­maceu­ti­cal sta­bi­li­ty and accu­ra­te shelf-life esti­ma­ti­on demands meti­cu­lous atten­ti­on to details. Lever­aging tools such as TIBCO Statistica’s Sta­bi­li­ty ana­ly­sis exten­si­on node and Alteryx’s sta­bi­li­ty ana­ly­sis tool revo­lu­tio­ni­zes this pro­cess, offe­ring stream­li­ned solu­ti­ons that save valuable time, resour­ces, and miti­ga­te the risk of errors. As we navi­ga­te the com­ple­xi­ties of phar­maceu­ti­cal sta­bi­li­ty test­ing, embra­cing inno­va­ti­ve solu­ti­ons beco­mes para­mount, ensu­ring the deli­very of safe, effec­ti­ve, and high-qua­li­ty pro­ducts to con­su­mers world­wi­de.

Cont­act us if you would like to find out more about the pos­si­bi­li­ties for sta­bi­li­ty ana­ly­ses!

ICH Gui­de­lines for Sta­bi­li­ty Test­ing
Rese­arch Paper: Line­ar Regres­si­on for Sta­bi­li­ty Test­ing

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