Machine learning methods for prediction of food effects on bioavailability: A comparison of Support Vector Machines and Artificial Neural Networks

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties

A retrospective biopharmaceutical analysis of >800 approved oral drug products: Are drug properties of solid dispersions and lipid-based formulations distinctive?

Increasing numbers of poorly water soluble drugs in development has intensified need for bio-enabling formulations including Lipid-Based Formulations (LBF) and Solid Dispersions (SD). Resultantly, a data-driven approach is required to increase formulation development efficiency. This review provides a retrospective analysis of molecular and biopharmaceutical properties of drugs commercialised as LBFs or SDs. A comprehensive stepwise statistical analysis of LBF and SD drug properties was conducted and compared to drugs not commercialised via either technology (Others), aiming to identify key predictors of successful formulation development. This review demonstrates LBF and SD drugs differ significantly in molecular weight, polar surface area, rotatable bonds and hydrogen bond acceptor count. Meanwhile, LBF and SD drugs display significantly different aqueous solubility, lipophilicity, size, molecular flexibility, hydrogen bonding capacity and rule-of-5 violations versus Others. LBF and SDs were 3 and 5 times more likely to display >1 rule-of-5 violation versus Others, over 55% of LBF drugs exceeded the reported melting point guide of 10 Hydrogen Bond Acceptors. Overall, by focusing on successfully commercialised drugs, this review provides improved understanding of links between drug properties and successful SD/LBF approaches, providing a framework for guiding pharmaceutical development on formulation approaches

In Silico, in Vitro, and in Vivo Evaluation of Precipitation Inhibitors in Supersaturated Lipid-Based Formulations of Venetoclax

The concept of using precipitation inhibitors (PIs) to sustain supersaturation is well established for amorphous formulations but less in the case of lipid-based formulations (LBF). This study applied a systematic in silico-in vitro-in vivo approach to assess the merits of incorporating PIs in supersaturated LBFs (sLBF) using the model drug venetoclax. sLBFs containing hydroxypropyl methylcellulose (HPMC), hydroxypropyl methylcellulose acetate succinate (HPMCAS), polyvinylpyrrolidone (PVP), PVP-co-vinyl acetate (PVP/VA), Pluronic F108, and Eudragit EPO were assessed in silico calculating a drug-excipient mixing enthalpy, in vitro using a PI solvent shift test, and finally, bioavailability was assessed in vivo in landrace pigs. The estimation of pure interaction enthalpies of the drug and the excipient was deemed useful in determining the most promising PIs for venetoclax. The sLBF alone (i.e., no PI present) displayed a high initial drug concentration in the aqueous phase during in vitro screening. sLBF with Pluronic F108 displayed the highest venetoclax concentration in the aqueous phase and sLBF with Eudragit EPO the lowest. In vivo, the sLBF alone showed the highest bioavailability of 26.3 ± 14.2%. Interestingly, a trend toward a decreasing bioavailability was observed for sLBF containing PIs, with PVP/VA being significantly lower compared to sLBF alone. In conclusion, the ability of a sLBF to generate supersaturated concentrations of venetoclax in vitro was translated into increased absorption in vivo. While in silico and in vitro PI screening suggested benefits in terms of prolonged supersaturation, the addition of a PI did not increase in vivo bioavailability. The findings of this study are of particular relevance to pre-clinical drug development, where the high in vivo exposure of venetoclax was achieved using a sLBF approach, and despite the perceived risk of drug precipitation from a sLBF, including a PI may not be merited in all cases

Characterization of gastrointestinal transit and luminal conditions in pigs using a telemetric motility capsule

Within preclinical research, the pig has become an important model in regulatory toxicology and pharmacokinetics, to assess oral dosage forms and to compare different formulation strategies. In addition, there are emerging application of the pig model to asses clinical dosing conditions in the fasted and fed state. In this study, the gastrointestinal transit conditions in male landrace pigs were studied with a telemetric motility capsule under fasted and postprandial conditions. The whole gut transit time (WGTT) was determined by administering a SmartPill® capsule to four landrace pigs, under both fasted and fed state conditions in a cross-over study design. Overall, this study found that small intestinal transit in landrace pigs ranged from 2.3 – 4.0 h, and was broadly similar to reported human estimates and was not affected by the intake conditions. Gastric emptying was highly variable and prolonged in landrace pigs ranging from 20 – 233 h and up to 264 h in one specific case. Under dynamic conditions pigs have a low gastric pH comparable to humans, however a high variability under fasted conditions could be observed. The comparison of the data from this study with a recent similar study in beagle dogs revealed major differences between gastric maximum pressures observed in landrace pigs and dogs. In the porcine stomach maximum pressures of up to 402 mbar were observed, which are comparable to reported human data. Intestinal maximum pressures in landrace pigs were in the same range as in humans. Overall, the study provides new insights of gastrointestinal conditions in landrace pigs, which can lead to more accurate interpretation of in vivo results obtained of pharmacokinetic studies in preclinical models. While small intestinal transit conditions, GI pH and pressures were similar to humans, the prolonged gastric emptying observed in pigs need to be considered in assessing the suitability of the pig model for assessing in vivo performance of large non-disintegrated oral drug products.

Applying computational predictions of biorelevant solubility ratio upon self-emulsifying lipid-based formulations dispersion to predict dose number

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SRMC (r2 = 0.81) and SRLC (r2 = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SRMC r2 = 0.74; SRLC r2 = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (FArom_B). Through using the equations to inform drug developability classifications (DCS) for drugs that have already been licensed as lipid based formulations, merits for development with SEDDS was predicted for 2/3 drugs

Exploring porcine gastric and intestinal fluids using microscopic and solubility estimates: Impact of placebo self-emulsifying drug delivery system administration to inform bio-predictive in vitro tools

Validation and characterisation of in vitro and pre-clinical animal models to support bio-enabling formulation development is of paramount importance. In this work, post-mortem gastric and small intestinal fluids were collected in the fasted, fed state and at five sample-points post administration of a placebo Self-Emulsifying Drug Delivery System (SEDDS) in the fasted state to pigs. Cryo-TEM and Negative Stain-TEM were used for ultrastructure characterisation. Ex vivo solubility of fenofibrate was determined in the fasted-state, fed-state and post-SEDDS administration. Highest observed ex vivo drug solubility in intestinal fluids after SEDDS administration was used for optimising the biorelevant in vitro conditions to determine maximum solubility. Under microscopic evaluation, fasted, fed and SEDDS fluids resulted in different colloidal structures. Drug solubility appeared highest 1 hour post SEDDS administration, corresponding with presence of SEDDS lipid droplets. A 1:200 dispersion of SEDDS in biorelevant media matched the highest observed ex vivo solubility upon SEDDS administration. Overall, impacts of this study include increasing evidence for the pig preclinical model to mimic drug solubility in humans, observations that SEDDS administration may poorly mimic colloidal structures observed under fed state, while microscopic and solubility porcine assessments provided a framework for increasingly bio-predictive in vitro tools

Computational pharmaceutics approaches to inform drug developability: focus on lipid-based formulations

Purpose: Declining productivity in the face of increasing numbers of poorly water-soluble drugs has fast-tracked necessity for predictive tools which assess the delivery potential of bio-enabling formulations. However, there is a perceived risk associated with early-stage selection of bio-enabling formulations. Computational pharmaceutics is a growing area of research interest to support structured guidance in formulation strategy. Using data-driven modelling, a streamlined roadmap of computational possibilities for development scientists is possible. Accordingly, the aim of this thesis was to examine the application of machine learning (ML) computational modelling to inform candidate developability. Via prediction of both quality target product profile characteristics and formulation performance indicators for lipid-based formulations (LBF). In recognition of the fact that computational models will not entirely circumvent need for manual screening, a further aim was to explore if analysis of landrace pig gastrointestinal fluids could facilitate increased bio-predictive performance of in vitro tools for LBFs. Methods: Data-driven computational models using various ML algorithms, with both classification and regression outputs, were developed to predict food effect on bioavailability, solubility ratio (SR) upon self-emulsifying drug delivery system (SEDDS) dispersion and apparent degree of supersaturation (aDS) ratio in supersaturated LBFs (sLBF). Model performance was validated using test sets or comparisons to ex vivo results. Gastrointestinal fluids from the landrace pig were collected in the fasted state, fed state and post placebo SEDDS administration. Ex vivo solubility analysis and microscopic imaging were completed using these fluids, where in vitro biorelevant dispersion screening with SEDDS using various dilution conditions was compared to the ex vivo results. Results: Firstly, this thesis demonstrated the applicability of ML for the prediction of a quality target product profile characteristic of interest in early development, namely food effect on bioavailability. Secondly, this thesis has advanced computational pharmaceutics to inform drug developability. Computational predictions of solubility gain upon SEDDS dispersion informed a biopharmaceutical dose number in intestinal fluids, which can be incorporated within the developability classification system (DCS) framework to inform drug developability. Thirdly, in recognition of the use of supersaturated LBFs to overcome dose loading limitations, this thesis has demonstrated how ML algorithms can predict the maximum dose loading upon thermal induced supersaturation. Moreover, increased understanding of the fate of SEDDS upon oral administration furthered the utility of the pig pre-clinical model, validated accuracy of in silico predictions and aided development of a bio-predictive in vitro screening tool for LBFs. Ultimately, it was demonstrated that the computational and in vitro tools developed in this thesis can be embedded within a wider refined drug substance to drug product development framework. Conclusion: This thesis highlighted how pharmaceutics datasets are amenable to ML. The ability of computational pharmaceutics to facilitate structured formulation decisions was demonstrated. As model development aided increased understanding of the investigated phenomena through their relationship to drug properties, this thesis identified the significant potential to be gained from early analysis of drug properties. Additionally, utility of the landrace pig model to inform increasingly bio-predictive in vitro screening tools was established. The proposed refining of the drug substance to drug product development framework demonstrated the significance of both computationally informed and experimentally confirmed aspects of drug developability decision-making

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions

Novel Biphasic Lipolysis Method to Predict in Vivo Performance of Lipid-Based Formulations

The absence of an intestinal absorption sink is a significant weakness of standard in vitro lipolysis methods, potentially leading to poor prediction of in vivo performance and an overestimation of drug precipitation. In addition, the majority of the described lipolysis methods only attempt to simulate intestinal conditions, thus overlooking any supersaturation or precipitation of ionizable drugs as they transition from the acidic gastric environment to the more neutral conditions of the intestine. The aim of this study was to develop a novel lipolysis method incorporating a two-stage gastric-to-intestinal transition and an absorptive compartment to reliably predict in vivo performance of lipid-based formulations (LBFs). Drug absorption was mimicked by in situ quantification of drug partitioning into a decanol layer. The method was used to characterize LBFs from four studies described in the literature, involving three model drugs (i.e., nilotinib, fenofibrate, and danazol) where in vivo bioavailability data have previously been reported. The results from the novel biphasic lipolysis method were compared to those of the standard pH-stat method in terms of reliability for predicting the in vivo performance. For three of the studies, the novel biphasic lipolysis method more reliably predicted the in vivo bioavailability compared to the standard pH-stat method. In contrast, the standard pH-stat method was found to produce more predictive results for one study involving a series of LBFs composed of the soybean oil, glyceryl monolinoleate (Maisine CC), Kolliphor EL, and ethanol. This result was surprising and could reflect that increasing concentrations of ethanol (as a cosolvent) in the formulations may have resulted in greater partitioning of the drug into the decanol absorptive compartment. In addition to the improved predictivity for most of the investigated systems, this biphasic lipolysis method also uses in situ analysis and avoids time- and resource-intensive sample analysis steps, thereby facilitating a higher throughput capacity and biorelevant approach for characterization of LBFs