How predictive tools can help with drug development

22 October 2018 11:00

Here, Rob Harris, chief technical officer, and Matt Ling, technical director, Juniper Pharma Services (now a part of Catalent Pharma Solutions), explains why modelling can help to minimise the risks associated with the development of poorly soluble drugs.

Whether it’s quickly checking the weather forecast or using a travel app to choose the fastest route home, the use of predictive technology is fast becoming a prominent feature of our lives.

In the pharmaceutical industry the use of predictive applications is also starting to change the way we develop medicines. For instance, predictive technologies are now helping to improve the speed and accuracy of decision-making in formulation strategy.

A promising orally administered drug candidate must be sufficiently soluble within the aqueous environment of the gastro-intestinal (GI) tract to be absorbed into the body. However, it is estimated that as many as 90% of new drug compounds have poor water solubility,¹ presenting a significant challenge for producing an effective medicine.

Fortunately, this problem can be addressed through the use of solubility-enhancing techniques, which can improve the solubility of even the most challenging drugs. Dosage forms such as amorphous solid dispersions, lipid-based self-emulsifying systems and nanoparticle systems can all provide a much-needed boost to solubility.

But which solubility-enhancing technique is best suited for your drug? Rigorous in vivo testing of various formulation types is both time consuming and costly. An approach that is being adopted by pharmaceutical scientists is the use of modelling software to simulate the behaviour of drugs. These can predict the stability of drugs in formulations,^2,3 in addition to predicting drug absorption and clearance from the body.^4,5,6

By inputting the known or calculated pharmacokinetic characteristics and physicochemical properties of a compound into a computer model that simulates the human body, the virtual system can predict the uptake and clearance of the drug. This predictive software can enable drug developers to assess the merits of different formulation types, narrowing the scope of required experimental assessment. The information obtained can then feed into the formulation development strategy and help assess the overall risk of development. Moreover, modelling helps identify information gaps in preclinical studies, informing the drug developer about what further experimental work needs to be undertaken.

Another important area of drug development where modelling software can assist is in designing and developing formulations for specific patient populations, which may have different physiological characteristics. For example, a formulation specifically for the treatment of young children must account for the physiological differences compared with adults (such as rate of absorption and metabolism), which can impact on the pharmacokinetics of the drug, influencing its oral bioavailability and overall efficacy.

While software modelling is proving to be a valuable tool for assisting drug developers in streamlining drug development programmes, the accuracy of the simulations generated will always depend on the quality of the data applied to the model. Insufficient or inaccurate data will result in a model that does not reflect the actual in vivo activity of the drug. It is therefore imperative that drug developers are armed with sufficient and accurate information to input into the model.

The high proportion of practically insoluble drugs emerging from drug discovery pipelines is showing no signs of slowing anytime soon. The developmental hurdles created by these poorly soluble drugs will place a burden on those involved in the process and increases risk of extended timelines and extra costs. Modelling systems are useful tools that generate meaningful data to alleviate the time-consuming in vivo R&D required for understanding and improving drug efficacy, mitigating the risks associated with drug development.

_References:

_{Am. Pharm. Rev., April 2013, 16(3).}
_{AAPS Pharm. Sci. Tech., 2011:12(3):932–937.}
_{Mol. Pharmaceutics., 2018:15(5):1826–1841.}
_{Mol. Pharmaceutics., 2016:13:3206–3215.}
_{Acta Pharm. Sin B. 2016:6(5):430–440.}
_{Drug Metab. Dispos., 2015:43:1823–1837.}