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174 Disha Tewari et al.
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Anchal Sharma, Nitish Kumar
✶
, Jyoti, Aanchal Khanna,
and Preet Mohinder Singh Bedi
✶
9 Computational prediction of drug-limited
solubility and CYP450-mediated
biotransformation
Abstract: In drug discovery and development, predicting drug properties greatly influ-
ences the selection and optimization of lead compounds. Drug solubility and biotransfor-
mation by cytochrome P450 (CYP450) enzymes are crucial in determining the success or
failure of drug development. Accurate computational prediction of these properties aids
in early identification and prioritization of candidates with desirable pharmacokinetic
profiles. Limited solubility poses a common challenge in drug development, as poorly
soluble compounds often have reduced bioavailability and unfavorable pharmacokinetic
and pharmacodynamic profiles. Computational methods can evaluate drug solubility by
predicting physicochemical descriptors like lipophilicity, molecular weight, hydrogen
bonding potential, and polar surface area. Machine learning algorithms aid in building
predictive models for drug solubility. Predicting solubility computationally enables early
identification of solubility issues, allowing timely modifications to enhance solubility
and increase success chances during clinical development. This chapter discusses about
the various models and tools used for CYP450 and solubility prediction with their accu-
racy rate, specificity, sensitivity and R
2
value in contrast to their cross-validation results.
By considering predictions of these two properties, researchers can prioritize com-
pounds with favorable solubility profiles and low susceptibility to CYP450-mediated me-
tabolism. This integrated computational approach saves time and resources while
improving drug discovery and development efficacy by selecting candidates with higher
success likelihood in later stages of the pipeline.
Keywords: CYP450 toxicity, ADME, solubility, prediction, artificial intelligence, ma-
chine learning model and metabolism
✶
Corresponding author: Nitish Kumar, Department of Pharmaceutical Sciences, Guru Nanak Dev
University, Amritsar 143005, Punjab, India, e-mail: nitishsanotra@live.com
✶
Corresponding author: Preet Mohinder Singh Bedi, Department of Pharmaceutical Sciences, Guru
Nanak Dev University, Amritsar 143005, Punjab, India, e-mail: preet.pharma@gndu.ac.in
Anchal Sharma, Jyoti, Aanchal Khanna, Department of Pharmaceutical Sciences, Guru Nanak Dev
University, Amritsar 143005, Punjab, India
Acknowledgment: The authors are grateful to Indian Council of Medical Research (ICMR-BMI/11(04)/
2022) for providing financial support to NK and Guru Nanak Dev University, Amritsar.
https://doi.org/10.1515/9783111207117-009
https://t.me/med1917
9.1 Introduction
Prediction of molecular properties has attracted attention and has led computer sci-
ence, physics, or chemistry to work in collaboration to speed up the process of drug
discovery. This has enabled researchers to discover molecules with desired properties
and reduce the chances of failure of drug molecule during clinical study [1]. Undoubt-
edly, key properties such as adequate solubility and toxicity have played a major role
in increasing the likelihood of a compound to be successful in clinical trials [2].
The combined effects of permeability and solubility contribute to achieving the
highest possible rate of passive drug transport across biological membranes. Addition-
ally, solubility holds considerable significance in drug disposition and several failed
medication development attempts have been attributed to poor solubility. Solubility is
a part of the Biopharmaceutical Classification System (BCS) and is crucial for quick
release BCS class II medications, whose absorption is constrained by their solubility
(thermodynamic barrier) or dissolution rate (kinetic barrier). Furthermore, incorrect
prediction of solubility can potentially compromise structure-activity relationship
(SAR) studies and lead to the misinterpretation of data in various in vitro experi-
ments [3].
The primary factors contributing to low aqueous solubility are elevated lipophilic-
ity and intense intermolecular interactions. The definitions of “good” and “poor” solu-
bility vary based on the predicted therapeutic dose and potency. According to the
general rule from the delivery perspective, a substance with an average potency of
1 mg/kg is generally considered to have sufficient solubility if it reaches a minimum
solubility of 0.1 g/L [4].
Appropriate formulation work can frequently overcome poor aqueous solubility.
This strategy, however, is expensive and has no assurance of success. It is far prefera-
ble to increase solubility by appropriate chemistry-based alterations to the molecule.
To achieve this, it is preferred to determine a candidate’s aqueous solubility as soon
as it is feasible during the discovery phase. Despite the recent availability of higher
throughput assays, good, solubility data generation is still a relatively costly and time-
consuming operation [5].
In addition to having excellent pharmacological effects and being suitably safe,
the newly synthesized drugs should aim to reduce the likelihood of drug-drug interac-
tions (DDIs) as the se interactions can negatively impact the effi cacy of concurrently
administered medications. The majority of DDIs are caused by CYP450 enzymes,
which are crucial for the phase I metabolism of many xenobiotics including the ma-
jority of commercially available medications. Although 57 CYP genes have been dis-
covered in humans so far, among them only six CYP isoforms: CYP1A2, CYP2B6,
CYP2C9, CYP2C19, CYP2D6, and CYP3A4; are capable of metabolizing the majority of
medications that have been approved by the Food and Drug Administration (FDA).
FDA regulations mandate that medications be tested for CYP1A2, 2B6, 2C8, 2C9, 2C19 ,
2D6, and 3A4/5 inhibitions and for CYP1A2, 2B6, 2C, and 3A inductions. CYP inhibition
176 Anchal Sharma et al.
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accounts for 70% of DDIs caused by pharmaceuticals that undergo CYP metabolism
[6]. Drug administration-related CYP inhibition or induction can result in unwanted
side effects with raised plasma concentrations or significant efficacy reductions with
decreased plasma concentrations for other co-administered medications [6]. To pre-
vent or decrease potential DDIs, several pharmaceutical companies run screening
tests on novel synthesized drug molecules. Currently, human liver microsomes or
hepatocytes are used in experimental evaluation of DDI risks related to the CYP me-
tabolism of drugs. In vitro assays can only provide limited information regarding the
SAR for CYP inhibition or induction, and they take a lot of time [7].
Thus, there has been a lot of interest in the development of models to predict the
water solubility and toxicity caused by CYP450 of drug candidates based on their chemi-
cal structure. Predictive models based on molecular descriptors, can provide important
information to better understand what features restrict solubility as well as toxicity [8].
Additionally, there is a growing need for accurate methods for predicting these charac-
teristics to achieve two main goals: first, to minimize the risk of late-stage attrition dur-
ing the design phase of new compounds and compound libraries; and second, to
optimize screening and testing by focusing on only the most promising compounds [9].
In contrast, computational predictions and in silico approaches are widely em-
ployed since they may be applied very early in the drug development process, are in-
expensive, and are capable of testing a large number of compounds. The number of
experimental trials needed for the selection of new drug candidates can be decreased
and the success rates can be raised by using these methodologies [10]. They can also
be used to forecast the behaviors of planned compounds that have not yet been pro-
duced synthetically [11].
9.2 Computational prediction of CYP450-mediated
drug toxicity
Toxicity is defined as the level to which a chemical substance or a particular blender
of material can harm a living being. Toxicity can refer to the reaction of an entire
organism, equally an animal, plant, or bacterium as well as the result on a substruc-
ture of the organism, the liver (hepatotoxicity), or cell cytotoxicity. Toxicity may be
systematic or target-specific that affect an organ of the body or the whole body. For
example, potassium cyanide enters systemic circulation and interferes with the ability
of cells to utilize oxygen, and causes target-specific toxicity (Figure 9.1) in the central
nervous system, kidney, and the hematopoietic system [12].
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9.2.1 Drug-induced toxicity
After the absorption of a drug from the gastrointestinal track, it undergoes first-pass
metabolism, where hepatic enzymes like CYP450 and their isoforms act on the drug
and convert the drug into its metabolites. This process is called biotransformation.
Some metabolites are stable and excrete out from the body but some are reactive me-
tabolites that accumulate in the cell, leading to cellular damage, and this leads to
drug-induced toxicity. During metabolic reaction, shown in Figure 9.2 of diclofenac,
the formation of quinonimine metabolite was found to be responsible for liver toxic-
ity [13]. Diclofenac, paracetamol, tebuquine, phenylamino alanine, amodiaquine,
phenacetin, nefazodone, resorufin, lapatinib, and buspirone generate quinonimine
metabolite under CYP450 and cause toxicity.
9.2.2 Drug-induced liver toxicity
Bioactivationisaprocessinwhichliverconvertdrugsintoitsstableorreactivemetabo-
lites. Stable metabolites are excreted out from the body and reactive metabolites accumu-
late in the hepatocytes and lead to the toxicity called drug-induced liver toxicity [14].
Liver has a high capacity for both phases I and II biotransformation (Figure 9.3). CYP450
enzymesintheliverplayaprimaryroleinthemetabolism of an incredibly diverse range
Figure 9.1: Diagrammatical representation of the type of toxicants and their interaction with the target
molecule.
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of foreign compounds including therapeutic agents like acetaminophen [15]. At therapeu-
tic doses, acetaminophen is deactivated by glucuronylation and sulphation to metabolites,
which are rapidly excreted in urine. However, a proportion of the drug undergoes bioac-
tivation to N-acetyl-p-benzoquinoneimine (NAPQI) by CYP2E1, CYP1A2, and CYP3A4.
NAPQI is rapidly quenched by a spontaneous reaction with hepatic glutathione after a
therapeutic dose of acetaminophen. After a toxic (over) dose, glutathione depletion oc-
curs, which is an obligatory step for covalent binding and leads to liver toxicity [16].
Figure 9.2: Metabolic reaction of diclofenac showing the formation of metabolites that induce liver toxicity.
Reactive
metabolite
Stable metabolitetoxicity
Cellular accumulation
Phase 1/2
bioactivation
Drug
Nucleic acid
Enzyme
Receptor
Transport
protein
Signaling
proteins
Receptor
Autologous
proteins
hypersensitivity
apoptosis
narcosis
–
–
–
–
–
carcinogenicity
Bio inactivation
excretion
Figure 9.3: Flow chart of biotransformation and drug metabolism causing toxicity.
9 Computational prediction of drug-limited solubility 179
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9.2.3 Role of CADD and artificial intelligence in the prediction
of toxicity
Computer-aided drug design (CADD) helps to speed up the search for potential thera-
peutic compounds. Typically, safety is the most important factor to take into account
when developing a medicine. This covers a wide range of negative and undesirable
pharmacological effects that must be evaluated throughout the nonclinical and clini-
cal stages of any drug discovery process [17]. Drug development activities have greatly
benefited from the use of the current databases, which include PubChem, DrugBank,
GreenmolBD, ChEMBL Database, and toxicity prediction online services like toxicity
Checker, PreADMET, ProTox-II, eMolTox, TOXNET, ToxiM, ToxCast, and Tox21 [19–29].
However, approaches and algorithms are crucial in the creation of pharmaceuticals
since they address specific problems and shortfalls that must still be fixed for future
drug safety evaluation. Toxicity prediction is time- and money-efficient when in silico
techniques are used. This chapter provides a thorough introduction and places special
emphasis on the latest in silico toxicity prediction techniques [29].
Drug development and discovery have always been extremely susceptible to
chance and serendipity. However, artificial intelligence (AI) has hastened drug inno-
vation and development in a positive way to increase the likelihood of identifying
novel therapeutic candidates that can be commercialized in the future [30].
Machine learning (ML) is a crucial branch of AI that is applied in numerous tech-
nological domains, both commercially and academically. ML is a set of numeral algo-
rithms that is based on data acquisition. ML algorithms are used in the prediction of
drug-protein interaction, which discover the efficacy and safety and optimize the ac-
tivity of the formulation/compound [31].
To prevent hazardous effects, it is crucial to predict the toxicity of any medicinal
molecule, identify a compound’s toxicity, and reduce the expense of drug discovery.
Some web-based tools, like pkCSM, LimTox, admetSAR, along with Toxtree, are accessi-
ble to lower the cost. Modern AI-based approaches check for similarities between
compounds and predict the toxicity of the compound based on characteristics [33–36].
The Tox21 Dat a Challenge organized by the Environmental Protection Agency (EPA),
US Food National Institutes of Health and Drug Administration (FDA) was an inven-
tiveness to assess some computational techniques to predict the toxicity of 12,707 envi-
ronmental compounds and drugs. An ML algorithm named DeepTox outperformed all
procedures (99% accuracy) by recognizing static and dynamic characteristics within
the chemical interpretation of the molecules, like molecular weight and Van der
Waals volume, and could systematically forecast the toxicity of a molecule using 2,500
predefined toxicophore features [37–39].
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9.3 In silico prediction of CYP450-mediated toxicity
Batsford [38] developed a reliable QSAR screening system for CYP2C9 and CYP3A4 tox-
icity. Bioactivity data of 14,936 compounds were gathered from CYP bioactivity data-
base from National Institutes of Health Chemical Genomics Centre and was utilized to
train and validate the models. Various classification strategies were evaluated on bi-
nary fingerprints as well as traditional molecular descriptors. Three models (Fig-
ure 9.4), a classification tree with high interpretability and prediction balance on the
classes, a k-nearest neighbor model with high complexity and high predictivity to-
ward inactive comp ounds, and an N-nearest neighbor model with the best perfor-
mancetowardstheactives,producedthebest results for each isoform with fitting
value and cross validation of 0.85% specificity and 0.69% sensitivity. The models were
validated using an external set of more than 2,000 molecules for each isoform. The
model’s dependability and stability were proven by the external validation. The devel-
opment of comparable strategies for the remaining relevant CYP isoforms will be a
future perspective [39].
In Noor et al. [39], Tian et al. developed a computational tool called CypReact for com-
pleting the initial reactant prediction task. Particularly, CypReact accepts as input any
one of the nine of the most significa nt human CYP450 enzymes – CYP1A2, CYP2A6,
CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, or CYP3A4 – and accurately predicts
how the query molecule will react with that specific CYP450 enzyme (Figure 9.5). Cy-
pReact has demonstrated excellent performance in tests using a data set of 1,632 mole-
cules, each of which is regarded as a “plausible” reactant, with a (cross-validation)
Figure 9.4: Diagrammatical representation of QSAR-based system for CYP450 by Batsford [38].
9 Computational prediction of drug-limited solubility 181
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AUROC (area under the receiver operating characteristic curve) of 0.83 to 0.92. Addition-
ally, researchers demonstrated that CypReact outperforms other reactant prediction
tools like ADMET Predictor and (a reactant-predicting version of) SMARTCyp, whose av-
erage AUROCs are 0.75 and 0.53, respectively. The performance of CypReact still greatly
outperformed that of SMARTCyp and ADMET Predictor after we applied the learnt Cy-
pReact models to a newly unexplored group of compounds. These findings imply that
CypReact might be a crucial part of a suite of in silico tools for properly forecasting the
outcomes of human Phase I, Phase II, and microbial metabolism [40].
In Nembri et al. [40], Agahi et al. used three computational toxicology software pro-
grams – MetaTox, SwissADME, and prediction of activity spectra for substances (PASS)
online – to predict the toxicity of three mycotoxins (ZEA, -ZEL, and -ZEL) and the prod-
ucts that define their metabolomics profile (Figure 9.6). Each mycotoxin under study had
a total of 12 metabolites that were predicted by MetaTox including five from ZEA and
seven from -ZEL and -ZEL. SwissADME allowed for the physical-chemical analysis of
each component and the prediction of how each one would behave in terms of absorp-
tion, distribution, metabolism, and toxicity. A HMDB ID could be given to one of those
based on a similarity score. Last but not least, using the structural data that was pre-
sented as Pa values, PASS online offered a complete prediction of all compounds. The
findings show a moderate-to-high absorption by the digestive system, but it is unlikely to
enter the brain in its present form unless it is metabolized. The metabolomics profile of
ZEA, -ZEL, and -ZEL, as analyzed by in silico programs (MetaTox, SwissADME, and PASS
online), predicts alteration of systems, pathways, and mechanisms that ultimately results
in several toxic effects, providing an excellent sight prior to starting studies on in vitro
or in vivo assays and contributing to the 3Rs principle by reducing animal testing [41].
DDIs may result in drug toxicity, diminished pharmacological efficacy, or unfavor-
able medication responses. As part of the drug discovery and development process,
studies seeking to identify potential DDIs for investigational drugs involve an evalua-
Figure 9.5: Diagrammatical representation of computational prediction tool CypReact by [39].
182 Anchal Sharma et al.
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tion of the DDIs potential mediated by inhibition or induction of the most significant
drug-metabolizing CYP450 isoforms. In Tian et al. [41], Dmitriev et al. developed a com-
puter model for the seven most significant P450 cytochromes, CYP1A2, CYP2B6, CYP2C19,
CYP2C8, CYP2C9, and CYP2D6, to forecast the DDIs. PASS software was used and pairs of
substances multilevel neighborhoods of atoms (PoSMNA) descriptors were computed
using structural formulas to create SAR models that predict metabolism-mediated DDIs
(Figure 9.7) for pairs of molecules. As a training set, about 2,500 records on DDIs caused
by these cytochromes were employed. Both well-known medications and novel chemi-
cals that are not yet synthesized are subject to prediction. According to leave-one-out
cross-validation (LOO CV) techniques, the average accuracy of the prediction of DDIs
caused by different CYP450 isoforms was roughly 0.92. The developed SAR models,
which are freely accessible as an online resource, offer predictions of DDIs caused by
the most significant P450 cytochromes [42].
In this article [42], models to classify new in silico models for the identification of
possible time-dependent CYP3A4 inhibitors were developed. Both traditional ML and
deep learning models were built based on the CYP3A4 TDI data set that we manually
gathered from literature and databases. A balanced data set and the deep-learning
model used by GraphConv were shown to be advantageous through comparisons of
various sampling techniques, molecule representations, and machine learning meth-
ods. An external data set was screened to determine the best model’s generalizability,
and biological tests were used to confirm the predicted outcomes. Additionally, using
information gain and frequency analysis, a number of structural alarms pertinent to
CYP3A4 time-dependent inhibitors were discovered [43].
Figure 9.6: Diagrammatical representation of computational model software used by [40].
9 Computational prediction of drug-limited solubility 183
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