Sanjay Konagurthu , senior director , science & innovation , pharma services , at Thermo Fisher Scientific , looks at the increasing importance of in silico modeling in many areas of drug manufacturing
Accelerating & de-risking drug development with in silico modelling
Sanjay Konagurthu , senior director , science & innovation , pharma services , at Thermo Fisher Scientific , looks at the increasing importance of in silico modeling in many areas of drug manufacturing
Pharmaceutical companies are under ever-increasing pressure to advance their promising drug candidates through clinical development as safely , effectively and cost-efficiently as possible . Against this backdrop , in silico modelling has evolved from being a nice-to-have resource to a must-have tool across all stages of investigational drug R & D .
Although empirical models have long been the de facto standard in drug research and development , the value of in silico methods is well established . In fact , digital evidence generated by in silico models is now included — and expected — in almost all regulatory submissions . In some situations , data generated via computational modelling serves as the key source of evidence in drug development programmes and related regulatory submissions .
Predicting solubility & bioavailability enhancement
Some 70-90 % of new chemical entities ( NCEs ) in development pipelines are poorly soluble , which can lead to inadequate and variable bioavailability , and render the drug ineffective . One of the biggest barriers to addressing bioavailability issues is the lack of familiarity with the delivery mechanisms and excipient functionality that enhance the solubility of candidate molecules . This is a significant deficit because selection of the appropriate enabling technology is foundational to a successful formulation strategy .
The impact of formulation strategies on bioavailability and solubility must be considered from the early stages of formulation development to avoid costly errors during later stages of development . Historically , researchers have used a trial-and-error approach to select technologies and formulations for enhancing solubility and bioavailability .
Today , however , in silico models that simulate interactions between APIs and polymers replace empiricism with a more rational , efficient strategy . The inputs for these models are the specific molecular structure and physico-chemical properties of the compound and the unique target product profile .
These simulations can also help predict biopharmaceutical classification and developability classification , which is a practical consideration for formulation . The insights derived from the models help to determine the optimal solubility enhancement technology and excipient combination to improve bioavailability .
Another in silico application related to formulation is polymer selection for amorphous dispersions to improve the solubility and dissolution of compounds . This involves calculations leveraging proprietary algorithms that incorporate a variety of computational methods , including quantum mechanics , molecular dynamics , quantitative structure-activity relationships , statistical analysis and internally developed models .
The calculations are conducted for any given API . The output can then be matched with a series of excipients used for formulating amorphous dispersions , whether spray drying or hot melt extrusion , enabling researchers to rank-order the best excipients to select for experimental screening . Molecular dynamics simulations provide additional insight by calculating the interaction energy between any given drug and the polymer .
In addition , in silico adsorption , distribution , metabolism and excretion-pharmacokinetic ( ADME- PK ) modelling and simulations can be used to identify pathways for poorly soluble , low-permeability compounds in order to enhance their bioavailability . These capabilities reduce unnecessary experimentation , saving significant time and cost .
Shelf life & packaging determination
Determining product shelf-life is a regulatory requirement for pharmaceuticals and an important consideration for packaging decisions . The stability profile of a pharmaceutical entity is based primarily on the physico-chemical properties of the drug substance and drug product .
Predictive accelerated stability studies allow the long-term stability characteristics of a drug substance or drug product to be characterised from the extrapolation of the results of short-term studies that measure , track and quantify stability-indicating attributes , such as degradation , thermal properties , crystallinity , colour , viscosity and particle size .
This approach differs from traditional forced degradation because the studies are designed for predictive shelf-life determinations rather than
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