Selection of Injectable Drug Product Composition using Machine Learning Models (Preprint)

BACKGROUND As of July 2020, a Web of Science search of “machine learning (ML)” nested within the search of “pharmacokinetics or pharmacodynamics” yielded over 100 publications. The publications were categorized to one of the following areas of the pharmaceutical sciences: PK/PD, dose optimization, quantitative structure–activity relationship (QSAR); adverse drug event (ADE) prediction and drug–drug interactions(DDIs) and clinical trial simulation (CTS). Supervised classification, regression, or a combination of the two approaches are the most frequently used ML methods and QSAR emerged as the most frequent application area. Table 1 summarizes the pharmaceutical sciences applications and ML algorithms that have been investigated in the literature. As noted, previously several recent reviews have focused on identifying the most promising and appropriate application areas and ML tools. Mak et al. provided an accessible primer on ML concepts that included a case study that employed RFR for investigating the structure–activity relationships of inhibitors of the putrescine transporter of trypanosome parasites. Hutchinson et al. proposed an implementation framework with two hypothetical examples that utilized deep learning to perform global parameter sensitivity analysis and for combining PK parameters with imaging and omics data. Koch et al. used CART-based ML approaches for covariate selection in the context of a simulated data and clinically relevant example of phototherapy for bilirubinaemia in neonates.81 In a commentary, Chaturvedula et al. highlighted the use of genetic algorithms (GAs) for model selection in population modelling and deep learning for target identification in drug repurposing. OBJECTIVE Applying deep learning to mine the increasing datasets in drug discovery not only enables us to learn from the past but to predict future drug repurposing specially in solid dosage formulations. Deep learning was applied to predict successful pharmaceutical formulations by constructing regression models. One of the main difficulties in formulation prediction is the small dataset with imbalanced input space due to the limited experimental data. For better performance, the data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data were introduced. The DNNs were trained on the data of two types of pharmaceutical dosage forms, including oral fast disintegrating films (OFDF) and oral sustained release matrix tablets (SRMT). Machine learning (ML) is enabling leap-step advances in several fields including drug discovery and materials science. Machine learning is one of the most exciting research areas in recent years. Machine learning can make data-driven predictions with existed oral formulation experimental data, which provides a great opportunity for efficient oral formulation development1–6. A well-designed machine learning method can greatly speed the development, optimize oral formulations, save the cost, keep products consistency, and accumulate and preserve the specific knowledge and expertise from the experts in a well-defined domain1. Table 1 summarizes recent development of machine learning in oral formulation application. An expert system (ES) is an intelligent program with the ability to accumulate and preserve the knowledge and experiences of the experts in a specific area (e.g., pharmaceutical formulations)12. Machine Learning Tools Oral Formulation Design (Capsule/Tablet) Hybrid expert system with ANNs Hard gelatin capsule formulations Expert system (SeDeM Diagram) Orally disintegrating tablets Expert system with ANNs Osmotic pump tablets Ontology-based expert system Immediate release tablets ME_expert 2.0 Microemulsions formulation Fuzzy logic-based expert system Freeze-dried formulations Cubist and Random Forest Cyclodextrin formulations Table 1. Machine Learning Applications in Oral Formulation Design Parenteral routes of administration are four routes of injectables, namely a. subcutaneous (mostly under the skin) to release into patient’s systemic circulation, b. intramuscular (mostly in a muscle) injected deep into muscle layers of rich blood supply, c. intravenous (in a vein) dose directly, rapidly into systemic circulation and d. intrathecal (around spinal cord), directly into vertebral column to bypass blood brain barrier. One critical aspect of parenteral formulation is the pH level. Drug formulations should have a target pH as close as possible to physiological pH. The acceptable range is pH 2–11 for IV and intramuscular injections, and pH 4-9 for subcutaneous injection due to potential irritation issues. Recently, long-acting injectable (LAIs) that are amenable to parenteral administration can confer several advantages over conventional drug formulations, including increased patient compliance and bioavailability of drug14. Moreover, LAIs can be engineered to provide either local (e.g., Zilretta®) or systemic (e.g., Lupron Depot®) drug exposure over a prolonged period, making them ideal formulation strategies for the treatment of chronic diseases15. Several strategies have been investigated to inform decision-making and expedite the drug formulation development process. For instance, mathematical models have been used to describe and greatly enhance our understanding of drug release mechanisms16. However, the application of these empirical models is limited to post hoc analysis of the in vitro drug release profiles of LAIs,and they do not offer information on in vitro drug release from LAIs a priori. More recently, molecular dynamics simulations have been investigated17. These techniques have been useful in quantifying links between drug release rates and formulation parameters (including particle size and drug loading levels)18.While the development of these techniques is an active area of research, molecular scale simulations of entire drug delivery systems are computationally intensive. These approaches can be used to confer useful information on potential LAI systems; however, they cannot currently be used in place of experimental drug release assays14. Several studies have also investigated machine learning (ML) approaches19. METHODS Both drug and drug product components were used for representing the properties of active pharmaceutical ingredient (API) and inactive ingredients (excipient). All drugs׳ name was described with the 4 molecular descriptors, including molecular weight, logP, pKa, and solubility. The excipient types were encoded to different numbers. The excipient performance and process parameters include %purity, %recovery, %in vitro and in vivo release of drugs at different timepoints and granulation process, with percentage of some excipients. Data splitting strategy and hyper parameters of machine learning methods A three-dataset (training/validation/test datasets) splitting strategy was used. The training set is for training models, and the validation set is for tuning hyper-parameters to find the best model. The accuracy of the test set shows the prediction ability on unknown data. This strategy is widely adopted in machine learning. For each dosage form, the pharmaceutical data were split into three subsets, both the validation set and the test set include 20 formulations, the rest of the data were used to train the models. RESULTS There are many reasons to classify drugs, ranging from understanding the usefulness of types of drugs to formulating treatment plans based on properties such as solubilty, hydrophobicity or log P and pKa for biological drugs. The quality of each drug will be characterized by several screening methods as shown below in Figure 4-6. Regression and Deep learning are the type of representation learning with multiple levels of transform modules, which contains more parameters than other learning algorithms and requires more data for training. However, one of the main difficulties in pharmaceutical drug product prediction is the small dataset with imbalanced input space due to the limited experimental data. Each drug has only around 109 drug product compositions. There are 3 APIs in the subcutaneous injection dataset. And all he APIs include with 109 drug product compositions. Therefore, selecting representative datasets for training and test is very important for the drug product composition prediction. In our research, the specific evaluation criteria were introduced, and several data splitting methods were investigated. Moreover, deep learning was compared with other machine learning techniques for the drug product composition prediction( Zhang W et. al., Sedan G et.al.,Ouyang D et. al) [5-7]. Drug products are made up of different excipients involving injectable formulations. The experimental data were extracted from our published data in the patents and Web of Science. Three different searching terms of solubility in buffer, solvents and molecular weight were used for search literature about the development of Injectable formulations. The molecular descriptors were used for representing the properties of APIs. All drugs' name were described with the three molecular descriptors, including molecular weight, logP, and pKa. The excipient types were encoded to different numbers. The process parameters include lyophilization process and other excipient characteristics with compositions. A three-dataset (training/validation/test datasets) splitting strategy was used. The training set is for training models, and the validation set is for tuning hyper-parameters to find the best model. The accuracy of the test set shows the prediction