Solutions

Drug Formulation

How AI addresses common pain points experienced in the formulation stage of the drug development process.

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. 

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Situation

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. 

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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. 

The BIOiSIM® platform features a
robust data lake foundation, integrating:

1 trillion potential compounds search space for de novo synthesis and structural screening

1 trillion potential compounds search space for de novo synthesis and structural screening

Physiological data from 7 different animal species, plus humans

Physiological data from 7 different animal species, plus humans

Support for genomics data integration

Support for genomics data integration

More than 3,000,000 real compounds including proprietary data from multiple partnerships

More than 3,000,000 real compounds including proprietary data from multiple partnerships

Proprietary experimental data from scientific literature and other sources

Proprietary experimental data from scientific literature and other sources

Validation by real-world observed data

Validation by real-world observed data

Proof of Value

Using Molecular Simulation and Statistical Learning Methods in Low-Solubility Drug Formulation Design

Challenge

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

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

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

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.

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ML methods have the potential to provide the next transformative leap forward toward rapid polymer screening and formulation design.

- CEO, Biotech Client
Case studies

Additional VeriSIM Life Case Studies & Content

01

Accelerating SUD Therapy Discovery with AI/ML Driven BBB Permeability Predictions

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02

Predicting Patient-Specific Drug Bioavailability with AI

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Evolve your pipeline

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®