Drug Formulation
How AI addresses common pain points experienced in the formulation stage of the drug development process.
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Introduction
Drug formulation is one of the most critical parts of pharmaceutical development, helping to determine the very best way to deliver an active ingredient or molecule to patients. The drug formulation process seeks to determine the right combination of inactive substances and active pharmaceutical ingredients (API), evaluate a drug’s scalability for manufacturing, establish the most effective protocols for treatment, and identify the right form for the drug itself (e.g. tablet, capsule, oral suspension, injection, etc). Only then will a patient-ready end product be achieved.
Finding the ideal drug formulation is typically a complex, slow, laborious and expensive part of early phase R&D. A variety of pain points combine to create considerable challenges for pharma researchers, who have traditionally lacked an integrated approach for de-risking their early R&D decisions in bringing a new drug to market.
The VeriSIM Life advantage
Today, there’s good news for pharma researchers embarking on the drug formulation process. Innovations in AI-driven technologies have allowed for the streamlining and de-risking of many aspects of early-stage drug development, including the drug formulation process.
VeriSIM Life (VSL)’s drug decision engine, BIOiSIM®, is a computational platform deploying advanced artificial intelligence and machine learning, a proprietary big data foundation, and state-of-the-art mechanistic models to discover novel therapies from existing molecules.
BIOiSIM, and VSL’s associated services, can be used to problem-solve some of the most significant challenges facing early-stage drug R&D – meeting development timelines faster, at lower costs, and with better results than traditional approaches.
The BIOiSIM AI-driven technology framework supports the drug formulation process by addressing key pain points in the journey:
- Predicts drug solubility and model variations for individuals, including interactions between API and carrier, solubility parameters, and by simulating amorphous solid dispersions (ASD) formation and dissolution mechanisms.
- Predicts drug stability faster, and at a significantly lower cost, than human and animal studies, using molecular dynamics simulations which model and predict ASD physical stability.
- Uses advanced modeling software as a starting point for pinpointing the most appropriate dose for human consumption, helping elucidate the relationship between compound exposure and therapeutic effect, including immune response, therapeutic window, and safety and efficacy for compounds in Phase-1 trials.
- Reduces the need for costly, resource-intensive formulation studies on humans and animals, predicting computationally how the API interacts with virtual versions of animal and human subjects.
- Uses state-of-the-art quantum and molecular mechanics simulations to predict changes in the oral bioavailability of API due to formulations and co-administered food containing metal ions.
- Utilizes robust mechanistic models of intestinal transit to predict bioavailability and drug exposure for formulations designed to be absorbed in different parts of the gastrointestinal tract.
- Machine learning platform capacities can be implemented as for oral drug intake as for inhalation, transdermal, and topical routes of administration.
BIOiSIM, and its groundbreaking Translational Index™️ technology
which advances only the most promising drug candidates through R&D to investigational new drug (IND) application, offers actionable insights of unprecedented value to the drug development industry.
Combining thousands of validation data sets, multi-compartmental models, and its integrated AI/ML engine, BIOiSIM achieves superior physiological and biological relevance within three classes of therapeutics: small molecules, large molecules, and re-engineered viruses/gene therapy.
AtlasGEN™️ Novel Drug Designer benefits
The BIOiSIM® platform features a
robust data lake foundation, integrating:
Vast chemical search space: The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1012 compounds.
Super fast discovery: AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data X% more efficiently than molecular docking-based approaches.
Pre-validated hits: Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
1 trillion potential compounds search space for de novo synthesis and structural screening
Physiological data from 7 different animal species, plus humans
Support for genomics data integration
More than 3,000,000 real compounds including proprietary data from multiple partnerships
Proprietary experimental data from scientific literature and other sources
Validation by real-world observed data
Using Molecular Simulation and Statistical Learning Methods in Low-Solubility Drug Formulation Design
Challenge
Using Molecular Simulation and Statistical Learning Methods in Low-Solubility Drug Formulation Design
Predicting the API solubility with various carriers in the API–carrier mixture and the principal API–carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. However, experimental determination of these interactions, including solubility and dissolution mechanisms, are time-consuming, costly and reliant on trial and error.
Solution
To streamline the formulation design process, molecular modeling has been applied to simulate amorphous solid dispersions (ASD) properties and mechanisms in order to predict the API solubility of various carriers.
Methods
- Quantum mechanical methods elucidate the strength of API–carrier non-bonding interactions
- Molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms
- Statistical learning models predict a variety of drug formulation properties to help predict ASD solubility
- Other computational applications help accelerate lead compound development before clinical trial
Outcome
In silico research has demonstrated the viability of rational formulation design of low-solubility drugs. Pertinent theoretical groundwork, including modeling applications and limitations, have shown the prospective clinical benefit of accelerated ASD formulation. Read the full article.
ML methods have the potential to provide the next transformative leap forward toward rapid polymer screening and formulation design.
Additional VeriSIM Life Case Studies & Content
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Bring better drugs to market, faster, with AtlasGEN Novel Drug Designer
Now you can accelerate the discovery of new therapies based on existing compounds with VeriSIM Life’s AtlasGEN Drug Designer computational platform – purpose-built to decode chemistry and biology at scale. With the industry’s most generalistic AI platform, your innovation is no longer limited to experimental constraints.
Contact us today to schedule a demonstration of BIOiSIM’s AtlasGEN technology.
Bring better drugs to market, faster, with BIOiSIM®
Now you can accelerate the discovery of new therapies based on existing compounds with VeriSIM Life’s BIOiSIM® computational platform – purpose-built to decode chemistry and biology at scale. With the industry’s most generalistic AI platform, your innovation is no longer limited to experimental constraints.
Contact us today to schedule a demonstration of BIOiSIM®
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Introducing AtlasGEN™ Novel Drug Designer
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
