• Users Online: 116
  • Print this page
  • Email this page


 
 
Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 1  |  Issue : 2  |  Page : 143-152

Benchmarking the distribution coefficient of anticancer lead compounds using the predicted log D values of clinically approved chemotherapeutic drugs


1 Department of Medical Technology, Institute of Arts and Sciences, Far Eastern University, Manila, Philippines
2 Department of Biology, College of Arts and Sciences, University of the Philippines Manila, Manila, Philippines

Date of Submission11-Apr-2022
Date of Decision20-May-2022
Date of Acceptance28-May-2022
Date of Web Publication15-Jun-2022

Correspondence Address:
Asst. Prof. John Sylvester Nas
Department of Biology, College of Arts and Sciences, University of the Philippines Manila, Manila
Philippines
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpdtsm.jpdtsm_31_22

Rights and Permissions
  Abstract 


BACKGROUND: The distribution coefficient (Log D) can predict the solubility of a compound at a particular pH. In identifying lead compounds, Log D is helpful to predict the behavior, permeability, and clearance of a compound in the different organs.
AIM AND OBJECTIVE: This study examined the ability of Log D to discriminate cancer tissues from non-cancer tissues using the predicted Log D of various clinically approved anticancer drugs.
MATERIALS AND METHODS: We collected the information on the different anticancer drugs for breast, liver, kidney, lung small, lung non-small, prostate, and bone cancer from the National Cancer Institute. We predicted their Log D values at different pH of their respective tissues.
RESULTS: Results show that only the Log D values of breast and lung non-small cancer drugs in the cancer tissues were significantly different (p<0.05) from the Log D of the non-cancer tissue counterpart. Moreover, the Log D value of the normal and bone cancer tissues is significantly different (p<0.05) from the different normal and cancer tissues evaluated. Furthermore, the Log D values of small lung cancer tissues are significantly different (p<0.05) from normal and kidney cancer tissues, normal and liver cancer tissues, and normal non-small and lung cancer tissues.
CONCLUSION: These findings suggest that drugs that may be permeable in breast and lung non-small cancer tissues may not be permeable in their normal tissue counterpart. Additionally, bone and lung small cancer drugs may have low permeability with other tissues, indicating that the unintended effects may be low. However, since there is a low permeability in other organs, it may not be a good candidate for drug repurposing. These findings are yet inconclusive; hence, further investigation is needed to verify the results of this investigation.

Keywords: Cancer, chemotherapeutics, distribution coefficient, drug repurposing, in silico, pharmacokinetics


How to cite this article:
Eclarin PR, Yan PA, Paliza CL, Ibasan B, Basiloy PR, Gante NA, Reyes AN, Nas JS. Benchmarking the distribution coefficient of anticancer lead compounds using the predicted log D values of clinically approved chemotherapeutic drugs. J Prev Diagn Treat Strategies Med 2022;1:143-52

How to cite this URL:
Eclarin PR, Yan PA, Paliza CL, Ibasan B, Basiloy PR, Gante NA, Reyes AN, Nas JS. Benchmarking the distribution coefficient of anticancer lead compounds using the predicted log D values of clinically approved chemotherapeutic drugs. J Prev Diagn Treat Strategies Med [serial online] 2022 [cited 2022 Aug 9];1:143-52. Available from: http://www.jpdtsm.com/text.asp?2022/1/2/143/347545




  Introduction Top


Cancer develops by the continuous proliferation of old or damaged cells and the formation of new cells even though they are not needed.[1] The constant proliferation of these types of cells without stopping causes abnormal tissue masses known as tumors; not all types of cancers form tumors.[2] Cancer cells are detrimental to humans due to their ability to evade apoptosis and undergo angiogenesis, which further promotes the survival of these cancer cells.[3] There are various speculations about how cancer originates, but it was discovered that most cancers are caused by environmental exposure rather than inherited genetic factors.[4] A study has shown that in 2008, only 5%–10% of all cancer cases in the world were due to genetic defects, while the remaining 90%–95% were attributed to environmental and lifestyle factors.[5]

Chemotherapeutic treatments may employ one kind of drug or in combination with other medications.[6] Chemotherapeutic drugs target specific cells within the different phases of the cell cycle since cancer cells are vulnerable during mitosis.[7] The effectiveness of chemotherapeutic drugs varies on their mechanism of action; hence using a combination of drugs shows a promising result.[8] However, its cytotoxic property is evident in cancer cells and normal tissues.[9]

Moreover, the process of drug discovery in developing a new anticancer drug is slow and expensive.[10] Using animal models, such as Caenorhabditis elegans, for cancer drug development may be less expensive than the clinical trial but a little costly for preliminary screening.[11] With clinical trials being the most costly factor in drug development, their importance for safety and efficacy justifies the high amount of funding.[12] Since 2008, in over 320,000 clinical trials, 17.2% of Phase II trials (testing for efficacy) and 12.2% of Phase III trials (testing for clinical benefits) have been prematurely terminated due to inadequate planning.[13]

Since the high-throughput screening of drug candidates is costly and inefficient, in silico prediction for active therapeutics is mainly one way of screening existing compounds.[14] Moreover, for drug repurposing, an in silico-based approach may be efficient to identify new uses of drug molecules rationally. Therefore, it is a strategy used to research existing drugs, which have already been proven and tested on humans, and is then redirected based on a valid target molecule for a particular disease.[15]

Lipinski's rule of five (Ro5) during drug development is usually used to evaluate the compounds' predicted oral bioavailability.[16] In Ro5, the characteristics of the desired lead compound should have a molecular weight of <500 Da, no more than 5 H-bond donors, no more than 10 H-bond acceptors, and a partition coefficient (Log P) not >5).[17] This concept is used as a guide for lead optimization. This screening eliminates nondrug-like molecules significantly and enhances the library in terms of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.[18] The significance of ADMET properties plays an essential role in drug screening since it accounts for the failure of 60% of drug molecules during the drug development process, and early prediction of these properties would lead to a significant cost reduction in its research.[19]

The Log P is used to assess the distribution of a solute in two immiscible solvents.[20] It is used to determine the distribution of a chemical in a body and assess the efficiency of induced drugs in a patient.[21] The efficiency of drugs can be determined using the Log P by mixing the drug sample with octanol and water and then determining the concentration in each layer.[22] Since cancer cells are intracellularly alkaline and could produce an acidic environment, there are different pH levels under other organ systems depending on the existing condition of the organ. Kidney cancer cells possess an extracellular pH of 6.2–6.8, while prostate cancer cells have an extracellular pH of 6.4–7.5.[23],[24]

On the other hand, the distribution coefficient (Log D) is a more efficient method of assessing the distribution and solubility of compounds under different pH levels, as it considers the pH levels of molecules in an aqueous solution.[25] The pH levels need to be considered in drug production as body fluids in various organs vary.[26]


  Materials and Methods Top


Selection of chemotherapeutics

We evaluated all the clinically approved drugs for breast, kidney, liver, lung (small and nonsmall), prostate, cervix, and brain cancer. Our inclusion criteria in selecting the compounds are: (1) availability in the National Cancer Institute (cancer.gov), (2) the 3D structure of the compound is available in PubChem (https://pubchem.ncbi.nlm.nih.gov), (3) compounds with minor modification from the generic compound. Compounds sharing the same structure but with different names were considered duplicates and excluded from the study.

Distribution coefficient prediction

We used Chemaxon (Budapest, Hungary) to predict the Log D values for each drug depending on the pH of the organ (both normal and cancer). Since the pH available is only limited to whole numbers, we calculated for the exact Log D of the drug using the slope-intercept formula, as shown in Equation 1.

y = mx + b

Equation 1: Slope intercept formula.

We pooled the Log D of the drugs to generate the range and identify the outliers using box and whisker plots. After identifying outliers, we normalized the data to compute for the mean Log D in each pH. We evaluated the data set for normality and homogeneity using the Kolmogorov–Smirnov test and Levene's test. The mean Log D values of the drugs in each cancer tissue were compared to each other using one-way analysis of variance with LSD, HSD, and Scheffe's for the post hoc analysis. We used QI Macros 2018 in all the statistical analyses. We set the level of significance for all statistical tests at P < 0.05.


  Results Top


Selection of chemotherapeutics

A total of 93 compounds were included in the study, as shown in [Table 1]. We included 33 drugs for breast cancer, 9 for kidney cancer, 4 for liver, 6 for small lung cancer, 19 for nonsmall lung cancer, 17 for prostate, and 5 for bone cancer. We found that 11 drugs were recommended for more than one type of cancer tissue. These drugs were docetaxel (breast, lung nonsmall, and prostate), doxorubicin hydrochloride (breast, lung small, lung nonsmall, and bone), goserelin acetate (breast and prostate), methotrexate sodium (breast, lung small, and bone), olaparib (breast and prostate), paclitaxel (breast and lung nonsmall), Zoladex (goserelin) (breast and prostate), cabozantinib-s-malate (kidney and liver), everolimus (kidney and lung small), lenvatinib mesylate (kidney and liver), and sorafenib tosylate (kidney and liver).
Table 1: List of approved chemotherapeutic drugs for different cancer from National Cancer Institute

Click here to view


Comparison of the distribution coefficient values of the cancer drugs in normal versus cancer tissues in various pH

The Log D values of the different cancer drugs in breast and lung (nonsmall) cancer were significantly different from their normal tissue counterpart. At pH 6.3, the Log D value for the breast cancer tissue (1.10 ± 0.35) is significantly different from pH 7.7 to 8.0 for normal breast tissue (2.27 ± 0.35) (P < 0.05), as shown in [Figure 1]a. In addition, the Log D value of lung (nonsmall) cancer tissue at pH 6.5 (2.70 ± 0.23) is significantly different from pH 8.0 of normal lung (nonsmall) tissue (3.42 ± 0.18) (P < 0.05), as shown in [Figure 1]b.
Figure 1: Distribution of the distribution coefficient values of the different cancer drugs in different environmental pH of various cancer and normal tissue. (a) breast cancer versus normal breast tissue, (b) lung non-small cancer versus normal lung tissue, (c) lung small cancer versus normal lung tissue, (d) kidney cancer versus normal kidney tissue, (e) liver cancer versus normal liver tissue, (f) bone cancer versus normal bone tissue, (g) prostate cancer versus normal prostate tissue. The *indicates significant difference at P < 0.05. Log D: Distribution coefficient

Click here to view


Meanwhile, the Log D values of kidney, prostate, liver, lung (small), and bone cancer are comparable with their normal tissue counterparts, as shown in [Figure 1]c, [Figure 1]d, [Figure 1]e, [Figure 1]f, [Figure 1]g.

Comparison of the distribution coefficient values of the cancer drugs in different tissues

The Log D values of bone cancer tissues at pH 4.0–6.8 are significantly different from normal breast tissues at pH 7.7–8, breast cancer tissues from pH 6.3–6.9, normal kidney tissues at pH 7.4–7.5, kidney cancer tissues at pH 6.2–6.8, normal liver tissues at pH 7.0, liver cancer tissues from pH 6.3–6.9, normal nonsmall lung tissue at pH 7.4–8.0, nonsmall lung cancer tissues at pH 6.5–6.7, normal prostate tissues at pH 6.1–6.5, and prostate cancer tissues at 6.4–7.5 (P < 0.05), as shown in [Table 2]. Furthermore, the Log D values of normal bone tissues, at pH 7.2–8.4 are significantly different from normal breast tissues at pH 7.7–8, normal kidney tissues at pH 7.4–7.5, kidney cancer tissues at pH 6.2–6.8, normal liver tissues at pH 7.0, liver cancer tissues from pH 6.3–6.9, normal nonsmall lung tissue at pH 7.4–8.0 and nonsmall lung cancer tissues at pH 6.5–6.7 (P < 0.05), as shown in [Table 2].
Table 2: Comparison of Log D values of the cancer and normal tissues

Click here to view


The Log D values of small lung cancer tissues, at pH 6.5–6.7 are significantly different (P < 0.05) from normal kidney tissues at pH 7.4–7.5, kidney cancer tissues at pH 6.2–6.8, normal liver tissues at pH 7.0, liver cancer tissues from pH 6.3–6.9, normal nonsmall lung tissue at pH 7.4–8.0, normal nonsmall lung tissues at pH 7.4–8.0, and nonsmall lung cancer tissues at pH 6.5–6.7. In addition, the Log D value of small lung cancer tissue at pH 6.5 is significantly different (P < 0.05) from normal breast tissues at pH 7.7–8. On the other hand, Log D values of normal small lung tissues at pH 7.4–8 are significantly different (P < 0.05) from normal kidney tissues at pH 7.4–7.5, kidney cancer tissues at pH 6.2–6.8, normal liver tissues at pH 7.0, and liver cancer tissues from pH 6.3–6.9. Moreover, at pH 7.4, the Log D value of normal small lung tissue is significantly different (P < 0.05) from normal breast tissues at pH 7.7–8.0, normal nonsmall lung tissues at pH 7.4–8.0, and nonsmall lung cancer tissues at pH 6.5–6.7, as shown in [Table 2].


  Discussion Top


Log D provides an idea about the distribution and solubility of a certain compound through a lipophilic delivery pathway under a particular pH.[27] In this paper, the Log D of the breast cancer and lung nonsmall cancer were significantly different from the normal tissues, implying that breast and lung nonsmall cancer drugs may have lower permeability to the normal tissues. Hence, they may exhibit a weaker cytotoxic effect. The interaction of a compound to the cell membrane and its permeability has a massive influence on its cytotoxic property.[28] Besides, Log D can be used to predict the permeability of a compound in a nonpolar biological tissue.[27]

In this paper, seven tissues were studied representing different cancer tissues, namely, breast, liver, lung (small), lung (nonsmall), kidney, bone, and prostate. For each tissue, there were different Log D values observed in various experiments: lungs at pH 7.4 is −1.32, liver at pH 7.4 is −0.84–1.63, kidney at pH 7.4 is 1.8, breast at pH 7.4 −1.2–1.75, and prostate at pH 7.4 is −3.81–−1.97.[29],[30],[31],[32],[33] To the extent of our information, there was no experiment in bone tissues that have measured its Log D value.

Moreover, we found out that the Log D of the bone cancer drugs is significantly different from almost all the predicted Log D of the other tissues. Similarly, the Log D of small cancer tissues is not comparable with the kidney, liver, and nonsmall lung tissues. We hypothesized that cancer drugs used against bone cancer might have weaker unintended effects on the other tissues since their Log D value may not be favorable on the respective pH of the organs. Similarly, these drugs may not be a good candidate for drug repurposing due to the incompatible Log D value at the particular pH of the other organs, which may denote lower permeability. However, there were drugs that did not follow our hypothesis, for they are recommended for different types of cancer with significantly different Log D values for their respective pH. These drugs were goserelin, docetaxel, olaparib, methotrexate, cabozantinib-S, sorafenib, everolimus, lenvatinib, doxorubicin, and paclitaxel. We assessed the oral bioavailability, mode of administration, mechanism of action, and drug clearance rate of these drugs to gain insight into why these drugs did not abide by our hypothesis.

The Log D values of goserelin do not fall within the predicted Log D ranges. Apparently, goserelin violates Lipinski's Ro5, exceeding the molecular weight, hydrogen bond acceptor, hydrogen bond donor, and Log P value, indicating that absorption and bioavailability are most likely to be poor. Consequently, goserelin is given subcutaneously; hence, bypassing the first-pass metabolism, maintaining its concentration. The relatively preserved concentration of the drug delivered to the target tissues may be attributed to its potency. However, since the Log D value of goserelin does not fall within the computed Log D value in the breast and prostate, it may be possible that this compound may not be highly bioavailable in these tissues. Goserelin is a luteinizing hormone-releasing hormone (LHRH) agonist which initially stimulates the release of luteinizing hormone resulting in a transient elevation of androgen (in men) and estradiol (in women). However, the chronic administration of this medication causes the downregulation of LHRH receptors, subsequently inhibiting the release of LH and sex hormones. Due to its ability to inhibit androgen and estradiol, it is mainly used to treat prostate cancer and breast cancer.[34] Interestingly, the Log D values for goserelin are <3, the recommended Log D values for small compounds to permeate the blood–brain barrier (BBB).[35] Since the LHRH agonist acts on the hypothalamus, it quickly passes through the BBB.[36]

The Log D values of docetaxel are outside the predicted Log D ranges. Apparently, docetaxel violates the Lipinski's Ro5, exceeding the molecular weight and hydrogen bond acceptor, indicating that it has a low oral bioavailability. Consequently, docetaxel is typically given intravenously; hence, bypassing the first-pass metabolism, maintaining its concentration.[37] The relatively sustained concentration of the drug delivered to the target tissues may be attributed for its potency. However, since the Log D value of docetaxel does not fall within the computed Log D ranges in the breast, lung (nonsmall), and prostate tissues, it is hypothesized that this compound may not be highly bioavailable in these tissues. A study has shown that the bioavailability of docetaxel is 1025.62 μg/mL h and has a drug clearance of 1.5 mL/min.[38]

The Log P values of olaparib are outside the predicted Log D ranges. It follows the Lipinski's Ro5, having a 434.5 g/mol molecular weight, a Log P of 1.9, 1 hydrogen bond donor, and five hydrogen bond acceptors, indicating that it has high absorption and bioavailability; hence, it is administered orally. However, since the Log D value of olaparib does not fall within the computed Log D ranges in breast cancer tissues and normal prostate tissues, probably, this compound may not be highly bioavailable in these tissues at certain pH. Olaparib is reported to inhibit the poly ADP-ribose polymerase enzyme, an enzyme known to repair DNA damage in both healthy and cancer cells.[39] Its inhibition properties to this enzyme make it difficult for cancers with BRCA1 and BRCA2 mutations to fix DNA damage, making some cancer cells less likely to survive.[40]

Methotrexate sodium deviates outside of the predicted Log D ranges. It also violates Lipinski's Ro5 by having a hydrogen bond acceptor count of 12, exceeding the recommended limit of 10. Nonetheless, methotrexate sodium may be given orally or through parenteral administration. It may be administered alone with leucovorin as a rescue agent to prevent toxicity or in combination with other drugs in treating various cancers.[41] By injecting the drug, its maintained concentration may be responsible for its efficacy toward the target organ. It may also bypass the drug distribution, which may result in toxicity toward the nonspecific target tissues as the drug generally affects all types of cells. Methotrexate targets dividing cells and competitively binds with dihydrofolate reductase, an enzyme needed for DNA synthesis, repair, and cell proliferation, resulting in cell proliferation inhibition.[42] The oral bioavailability of methotrexate is noted to be 36% (±10%), while its bioavailability in the intramuscular injection is 93% (±14%).[43] The drug clearance rate is reported to vary widely yet is usually decreased when induced at high doses.[44] Its toxicity can be attributed to its delayed drug clearance, which can be reduced with the administration of leucovorin as a rescue agent.[45]

Sorafenib tosylate with the molecular weight of 637 g/mol exceeds the desired molecular weight in Lipinski's Rule of Five, which may affect its absorptivity. Despite this violation, the rest of its physicochemical properties fall within the desired range in Lipinski's rule of five, which may explain its oral bioavailability. Since the Log D values of sorafenib tosylate do not fall within the predicted Log D ranges in liver cancer tissues, this compound may not be permeable in these tissues. This predicted Log D contrasts with its bioavailability, which is relatively high (about 38–49% but reduces to 29% in a high-fat diet).[46] Moreover, sorafenib is a kinase inhibitor that prevents tumor growth and angiogenesis in both hepatocellular carcinoma (HCC) and advanced renal cell carcinoma (RCC).[47]

The Log D values of cabozantinib-S malate in liver and kidney cancer are within the predicted Log D ranges. Cabozantinib does not follow Lipinski's Ro5 since it has a 635.6 g/mol molecular weight and an H-bond acceptor of 12. Despite its violation of Lipinski's Ro5, it is still orally administered with a bioavailability ranging from 74% to 93% for capsules and 94% for tablets.[48] In a tumor microenvironment, some cancer cells produce hepatocyte growth factor (HGF), which stimulates and activates the MET pathway promoting the proliferation, migration, invasion, and survival of cancer cells.[49] Cabozantinib is a form of tyrosine kinase inhibitor that exhibits antitumor activity by targeting the MET tyrosine kinase receptor, the receptor for a HGF, inhibiting the angiogenic activity of HGF and tumor progression in liver cancer. Moreover, since cabozantinib modulates the HGF produced by cancer cells, its administration might not affect the undamaged normal liver tissues. One of the pathologic causes for angiogenesis in renal cell carcinoma is the inactivation of Von Hippel-Lindau, which is associated with the overexpression of vascular endothelial growth factor (VEGF) and leads to the activation of hypoxia-inducible factors (HIF-1α and HIF 2α), which stimulate blood vessel formation and promote tumor proliferation and metastasis. There are more than 800 target genes of HIF, and the most important one is VEGF.[50] Therefore, by blocking and preventing the overexpression of VEFG, cabozantinib has been proven helpful in treating kidney cancer such as RCC. As of the present, there are no research claims that cabozantinib affects HGF and VEGF in normal cells.

The Log D values of everolimus (Afinitor) fall outside of the predicted Log D ranges for the kidney and lungs. Everolimus also violates Lipinski's rule of five in three factors: the molecular weight exceeds the 500 Daltons, its hydrogen bond acceptor count at 14, and the Log P value at 5.9. These sets of violations challenge the solubility and bioavailability of the compound when taken orally. Despite these violations, everolimus is still administered orally and used as a second-line treatment for advanced RCC behind sunitinib or sorafenib.[51] Everolimus has also been approved against neuroendocrine tumors (NETs) originating from the lung regions. In the 10 mg recommended dosage, over 75% of everolimus is distributed into the blood cells, and 98% of it is eliminated in the form of metabolites after it is metabolized in the liver and intestines. Outside of other modifying variables, around 0.20–0.15 mg of everolimus is left within the patient's system for actual medical function.[52] Finally, trough levels are ideally maintained at 3–8 ng/ml. However, up to 15 ng/ml values still fall within manageable levels of toxicity.[53] In a situation where everolimus has low bioavailability, even a small amount of the drug, ~20 μmol/l is sufficient to induce autophagy in renal cancer cells.[54] However, it must be noted that 39%–55% of lung cancer patients who use everolimus (10 mg) exhibited lung anomalies.[55]

The Log D values of lenvatinib mesylate fall outside the predicted Log D ranges in the kidney. However, it falls within the Log D ranges in the liver. Lenvatinib mesylate violates Lipinski's Ro5, having a molecular weight of 523 g/mol, slightly exceeding the 500 g/mol. Other than that, all other rules have not been violated. This indicates that there may be low absorption and low bioavailability for this drug, specifically in the kidneys. However, since lenvatinib mesylate falls within the Log D values of the liver, it is hypothesized that this compound may be highly bioavailable in this tissue. It is orally administered, having a peak plasma concentration 1-4 h postdosage, but with the administration with food, the rate of absorption was reduced and delayed for 2 h to 4 h. Lenvatinib mesylate is reported to inhibit VEGS receptors (VEGFR1, VEGFR2, and VEGF3) which subsequently inhibits mechanisms for tumor growth, angiogenesis, and cancer progression.[56] There are not many studies with the specific mechanism of action of lenvatinib mesylate on kidney cancer; however, its approval for kidney cancer treatment seems to be based on an open-label phase II trial.[57] Here, it was reported that 153 patients had renal cell carcinoma and had previously taken medications such as sunitinib which inhibits VEGF. Clinical trials of lenvatinib and its combination with everolimus revealed more toxicity than everolimus alone.[58] Therefore, lenvatinib is mostly used in combination with everolimus patients with advanced RCC after anti-angiogenic therapy for reduced toxicity.[58],[59]

The Log D values of doxorubicin are outside the predicted Log D ranges. Doxorubicin violates Lipinski's Ro5 due to its molecular weight being 543.5 g/mol, a Hydrogen bond acceptor count of 12, and a hydrogen bond donor count of 6, which may indicate poor absorption and low bioavailability. This instance may explain its intravenous administration, which bypasses the first-pass metabolism to maintain its concentration. The total drug clearance of doxorubicin is relatively low, about 12%, but in the breast, its flow rate is about 30.70 L/h and 0.66 L/h in the kidney.[60] The low total drug clearance of doxorubicin may result in high bioaccumulation long-term. Doxorubicin damages DNA strands with its ability to intercalate within DNA base pairs and inhibit the enzyme topoisomerase II and the DNA and RNA synthesis. It causes the induction of apoptosis.[61]

The Log D values of paclitaxel are mainly outside the Log D ranges. It violates Lipinski's Ro5 for having a molecular weight of 853.9 g/mol and a hydrogen bond acceptor count of 14, which exceeds the desired physicochemical properties. This indicates low permeability and poor solubility.[62] Hence, paclitaxel is typically administered intravenously, preserving its concentration in the systemic circulation. Even though paclitaxel has 89%–98% affinity to plasma in vitro, its drug clearance is low, which is only 1.89 ± 0.15 L/h/kg.[63],[64] With the low drug clearance, this drug may accumulate in the tissues chronically. With paclitaxel being an anti-microtubule agent, it prevents depolymerization by promoting the assembly of microtubules from tubulin dimers. It can polymerize tubulin in the absence of microtubule-associated proteins. In this way, cells cannot form a typical mitotic apparatus due to the blocked cells in the G2/M phase of the cell cycle caused by paclitaxel.[65]


  Conclusion Top


The use of Log D in screening compounds to discriminate against cancer tissues and noncancer tissues may not be consistent with all types of cancer. Although breast and nonsmall lung cancer drugs show statistically significant Log D for cancer tissues and their noncancer counterpart, the other cancer drugs demonstrate otherwise. We also compared the computed Log D ranges of the different types of cancer. We found that the bone and small lung cancer were significantly different from the other cancer tissues. There are two hypotheses generated from this observation. The first one is that cancer drugs in these organs may have weaker side effects in the other tissues. Second, candidate drugs used in these tissues may not be fit for repurposing the counterpart tissues. We pooled the list of medications and highlighted those which were used in two or more different tissues. We found out that most of these drugs fail to fall within the tissues' computed Log D ranges. Typically, these drugs are not administered orally; hence they maintain their dosage in the systemic circulation. In addition, the potency of these drugs may be attributed to their innate toxicity and mechanism of action. Altogether, our findings may be a helpful reference value for the preliminary screening of lead compounds. However, the certainty of establishing Log D as a parameter to discriminate cancer tissue from normal tissue may not still be evident. Hence, further investigations are needed to confirm our observations.

Limitation of the study

At present, this study only provides a computational prediction of the Log D values of the different chemotherapeutic drugs to streamline drug screening of lead compounds and drug repurposing. Actual experimentation is warranted to validate our result.

Ethical clearance

No ethical permission for this study was required. No animals and humans were involved in this study.

Availability of data and materials

The full list of approved drugs for various types of cancer can be accessed through the https://www.cancer.gov.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Matthews HK, Bertoli C, de Bruin RA. Cell cycle control in cancer. Nat Rev Mol Cell Biol 2022;23:74-88.  Back to cited text no. 1
    
2.
Cooper GM. The Cell: A Molecular Approach. 2nd ed. New York: Sunderland; 2000.  Back to cited text no. 2
    
3.
Fouad YA, Aanei C. Revisiting the hallmarks of cancer. Am J Cancer Res 2017;7:1016-36.  Back to cited text no. 3
    
4.
Blackadar CB. Historical review of the causes of cancer. World J Clin Oncol 2016;7:54-86.  Back to cited text no. 4
    
5.
Anand P, Kunnumakkara AB, Sundaram C, Harikumar KB, Tharakan ST, Lai OS, et al. Cancer is a preventable disease that requires major lifestyle changes. Pharm Res 2008;25:2097-116.  Back to cited text no. 5
    
6.
Bennardo L, Bennardo F, Giudice A, Passante M, Dastoli S, Morrone P, et al. Local chemotherapy as an adjuvant treatment in unresectable squamous cell carcinoma: What do we know so far? Curr Oncol 2021;28:2317-25.  Back to cited text no. 6
    
7.
Chan KS, Koh CG, Li HY. Mitosis-targeted anti-cancer therapies: Where they stand. Cell Death Dis 2012;3:e411.  Back to cited text no. 7
    
8.
Liang P., Ballou B., Lv X., Si W., Bruchez M.P., Huang W., Dong X. Monotherapy and combination therapy using anti-angiogenic nanoagents to fight cancer. Advanced Materials. 2021;33:1-20.  Back to cited text no. 8
    
9.
Garcia-Oliveira P, Otero P, Pereira AG, Chamorro F, Carpena M, Echave J, et al. Status and challenges of plant-anticancer compounds in cancer treatment. Pharmaceuticals (Basel) 2021;14:157.  Back to cited text no. 9
    
10.
Yap TA, Sandhu SK, Workman P, de Bono JS. Envisioning the future of early anticancer drug development. Nat Rev Cancer 2010;10:514-23.  Back to cited text no. 10
    
11.
Nas J, Dangeros S, Chen P, Dimapilis R, Gonzales D, Hamja F, Villanueva A. Evaluation of Anticancer Potential of Eleusine Indica Methanolic Leaf Extract through Ras-and Wnt-Related Pathways Using Transgenic Caenorhabditis Elegans Strains. J. Pharm. Negative Results 2020,11:42-46.  Back to cited text no. 11
    
12.
Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp Clin Trials Commun 2018;11:156-64.  Back to cited text no. 12
    
13.
Genkins A, Li G, Arlington S, Chew P. Clinical Trials Don't Have to Cost Too Much or Take Too Long. Pharmaceutical Executive; 2018. Available from: https://www.pharmexec.com/view/clinical-trials-dont-have-cost-too-much-or-take-too-long-1. [Last accessed on 2021 Feb 23].  Back to cited text no. 13
    
14.
Astashkina A, Mann B, Grainger DW. A critical evaluation of in vitro cell culture models for high-throughput drug screening and toxicity. Pharmacol Ther 2012;134:82-106.  Back to cited text no. 14
    
15.
Rudrapal MJ, Khairnar SG, Jadhav A. Drug repurposing (DR): An emerging approach in drug discovery. Badria FA, Editor. Drug Repurposing - Hypothesis, Molecular Aspects and Therapeutic Applications. London, England: IntechOpen; 2020.  Back to cited text no. 15
    
16.
Preedy VR, Watson RR. Nuts and Seeds in Health and Disease Prevention. 2nd ed. London, United Kingdom: Academic Press, An imprint of Elsevier; 2020.  Back to cited text no. 16
    
17.
Nas JS. Exploring the binding affinity and non-covalent interactions of anthocyanins with aging-related enzymes through molecular docking. Philipp J Health Res Dev 2020;24:9-19.  Back to cited text no. 17
    
18.
Kenakin TP. Pharmacology in Drug Discovery and Development: Understanding Drug Response. 2nd ed. London: Academic Press is an Imprint of Elsevier; 2017.  Back to cited text no. 18
    
19.
Puri M, Pathak Y, Sutariya V, Moreno W. Artificial Neural Network for Drug Design, Delivery and Disposition. Boston: Academic Press; 2016.  Back to cited text no. 19
    
20.
Speight JG. Reaction Mechanisms in Environmental Engineering: Analysis and Prediction. London, United Kingdom: Butterworth-Heinemann; 2018.  Back to cited text no. 20
    
21.
Johanson, G. Comprehensive Toxicology, 2nd ed., Vol. 1. Oxford: Elsevier Science; 2010. p. 153-177.  Back to cited text no. 21
    
22.
Bannan CC, Calabró G, Kyu DY, Mobley DL. Calculating partition coefficients of small molecules in octanol/water and cyclohexane/water. J Chem Theory Comput 2016;12:4015-24.  Back to cited text no. 22
    
23.
White KA, Grillo-Hill BK, Barber DL. Cancer cell behaviors mediated by dysregulated pH dynamics at a glance. J Cell Sci 2017;130:663-9.  Back to cited text no. 23
    
24.
Logozzi M, Capasso C, Di Raimo R, Del Prete S, Mizzoni D, Falchi M, et al. Prostate cancer cells and exosomes in acidic condition show increased carbonic anhydrase IX expression and activity. J Enzyme Inhib Med Chem 2019;34:272-8.  Back to cited text no. 24
    
25.
Bhal SK. Lipophilicity descriptors: Understanding when to use logP & logD. ACD/Labs PhysChem Software Application Notes; 2007.  Back to cited text no. 25
    
26.
Schmaljohann D. Thermo- and pH-responsive polymers in drug delivery. Adv Drug Deliv Rev 2006;58:1655-70.  Back to cited text no. 26
    
27.
Volkova TV, Simonova OR, Perlovich GL. Physicochemical profile of antiandrogen drug bicalutamide: Solubility, distribution, permeability. Pharmaceutics 2022;14:674.  Back to cited text no. 27
    
28.
Wytrwal M, Knobloch P, Lasota S, Michalik M, Nowakowska M, Kepczynski M. Effect of polycation nanostructures on cell membrane permeability and toxicity. Environ Sci Nano 2022;9:702-13.  Back to cited text no. 28
    
29.
Dutton B, Woods A, Sadler R, Prime D, Barlow DJ, Forbes B, et al. Using polar ion-pairs to control drug delivery to the airways of the lungs. Mol Pharm 2020;17:1482-90.  Back to cited text no. 29
    
30.
Greupink R, Dillen L, Monshouwer M, Huisman MT, Russel FG. Interaction of fluvastatin with the liver-specific Na+ -dependent taurocholate cotransporting polypeptide (NTCP). Eur J Pharm Sci 2011;44:487-96.  Back to cited text no. 30
    
31.
Araujo BV, Silva CF, Haas SE, Dalla Costa T. Microdialysis as a tool to determine free kidney levels of voriconazole in rodents: A model to study the technique feasibility for a moderately lipophilic drug. J Pharm Biomed Anal 2008;47:876-81.  Back to cited text no. 31
    
32.
Ahern TP, Lash TL, Damkier P, Christiansen PM, Cronin-Fenton DP. Statins and breast cancer prognosis: Evidence and opportunities. Lancet Oncol 2014;15:e461-8.  Back to cited text no. 32
    
33.
Harada N, Kimura H, Onoe S, Watanabe H, Matsuoka D, Arimitsu K, et al. Synthesis and biologic evaluation of novel 18F-labeled probes targeting prostate-specific membrane antigen for PET of prostate cancer. J Nucl Med 2016;57:1978-84.  Back to cited text no. 33
    
34.
Finkelstein JS, Lee H, Burnett-Bowie SA, Pallais JC, Yu EW, Borges LF, et al. Gonadal steroids and body composition, strength, and sexual function in men. N Engl J Med 2013;369:1011-22.  Back to cited text no. 34
    
35.
Fong CW. Permeability of the blood-brain barrier: Molecular mechanism of transport of drugs and physiologically important compounds. J Membr Biol 2015;248:651-69.  Back to cited text no. 35
    
36.
Begley DJ. In Handbook of Biologically Active Peptides. San Diego: Elsevier Academic Press; 2006. p. 1469-74.  Back to cited text no. 36
    
37.
Koolen SL, Oostendorp RL, Beijnen JH, Schellens JH, Huitema AD. Population pharmacokinetics of intravenously and orally administered docetaxel with or without co-administration of ritonavir in patients with advanced cancer. Br J Clin Pharmacol 2010;69:465-74.  Back to cited text no. 37
    
38.
Guo L, Peng H, Cai HL, Tang D, Hu H, Wang F, et al. Effect of aprepitant administration on CINV caused by cisplatin multi-day chemotherapy and pharmacokinetics of docetaxel. Cancer Chemother Pharmacol 2019;83:727-34.  Back to cited text no. 38
    
39.
Weil MK, Chen AP. PARP inhibitor treatment in ovarian and breast cancer. Curr Probl Cancer 2011;35:7-50.  Back to cited text no. 39
    
40.
Audeh MW, Carmichael J, Penson RT, Friedlander M, Powell B, Bell-McGuinn KM, et al. Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: A proof-of-concept trial. Lancet 2010;376:245-51.  Back to cited text no. 40
    
41.
Howard SC, McCormick J, Pui CH, Buddington RK, Harvey RD. Preventing and managing toxicities of high-dose methotrexate. Oncologist 2016;21:1471-82.  Back to cited text no. 41
    
42.
Skubisz MM, Tong S. The evolution of methotrexate as a treatment for ectopic pregnancy and gestational trophoblastic neoplasia: A review. ISRN Obstet Gynecol 2012;2012:637094.  Back to cited text no. 42
    
43.
Campbell MA, Perrier DG, Dorr RT, Alberts DS, Finley PR. Methotrexate: Bioavailability and pharmacokinetics. Cancer Treat Rep 1985;69:833-8.  Back to cited text no. 43
    
44.
Kramer E, Filtz M, Pace M. Evaluation of methotrexate clearance with an enteral urine alkalinization protocol for patients receiving high-dose methotrexate. J Oncol Pharm Pract 2021;27:26-32.  Back to cited text no. 44
    
45.
Zelcer S, Kellick M, Wexler LH, Gorlick R, Meyers PA. The Memorial Sloan Kettering Cancer Center experience with outpatient administration of high dose methotrexate with leucovorin rescue. Pediatr Blood Cancer 2008;50:1176-80.  Back to cited text no. 45
    
46.
Mousa AB. Sorafenib in the treatment of advanced hepatocellular carcinoma. Saudi J. Gastroenterol 2008;14:40.  Back to cited text no. 46
    
47.
Abdelgalil A.A., Alkahtani H.M., Al-Jenoobi F.I. Sorafenib. Profiles drug subst. Excip Relat Methodol 2019;44:239-266.  Back to cited text no. 47
    
48.
Hulin A, Stocco J, Bouattour M. Clinical pharmacokinetics and pharmacodynamics of transarterial chemoembolization and targeted therapies in hepatocellular carcinoma. Clin Pharmacokinet 2019;58:983-1014.  Back to cited text no. 48
    
49.
Owusu BY, Galemmo R, Janetka J, Klampfer L. Hepatocyte growth factor, a key tumor-promoting factor in the tumor microenvironment. Cancers (Basel) 2017;9:E35.  Back to cited text no. 49
    
50.
Abdelaziz A, Vaishampayan U. Cabozantinib for the treatment of kidney cancer. Expert Rev Anticancer Ther 2017;17:577-84.  Back to cited text no. 50
    
51.
Motzer RJ, Escudier B, Oudard S, Hutson TE, Porta C, Bracarda S, et al. Efficacy of everolimus in advanced renal cell carcinoma: A double-blind, randomised, placebo-controlled phase III trial. Lancet 2008;372:449-56.  Back to cited text no. 51
    
52.
Enna SJ, Bylund DB. xPharm: The Comprehensive Pharmacology Reference; Amsterdam: Elsevier; 2008. Available from: https://www.sciencedirect.com/science/referenceworks/9780080552323. [Last accessed on 2021 May 13].  Back to cited text no. 52
    
53.
Synold TW, Plets M, Tangen CM, Heath EI, Palapattu GS, Mack PC, et al. Everolimus exposure as a predictor of toxicity in renal cell cancer patients in the adjuvant setting: Results of a pharmacokinetic analysis for SWOG S0931 (EVEREST), a phase III study (NCT01120249). Kidney Cancer 2019;3:111-8.  Back to cited text no. 53
    
54.
Zeng Y, Tian X, Wang Q, He W, Fan J, Gou X. Attenuation of everolimus-induced cytotoxicity by a protective autophagic pathway involving ERK activation in renal cell carcinoma cells. Drug Des Devel Ther 2018;12:911-20.  Back to cited text no. 54
    
55.
Dejust S, Morland D, Bruna-Muraille C, Eymard JC, Yazbek G, Savoye AM, et al. Everolimus-induced pulmonary toxicity: Findings on 18F-FDG PET/CT imaging. Medicine (Baltimore) 2018;97:e12518.  Back to cited text no. 55
    
56.
Di Stefano M, Colombo C, De Leo S, Perrino M, Viganò M, Persani L, et al. High prevalence and conservative management of acute cholecystitis during lenvatinib for advanced thyroid cancer. Eur Thyroid J 2021;10:314-22.  Back to cited text no. 56
    
57.
Taylor MH, Lee CH, Makker V, Rasco D, Dutcus CE, Wu J, et al. Phase IB/II trial of lenvatinib plus pembrolizumab in patients with advanced renal cell carcinoma, endometrial cancer, and other selected advanced solid tumors. J Clin Oncol 2020;38:1154-63.  Back to cited text no. 57
    
58.
Leonetti A, Leonardi F, Bersanelli M, Buti S. Clinical use of lenvatinib in combination with everolimus for the treatment of advanced renal cell carcinoma. Ther Clin Risk Manag 2017;13:799-806.  Back to cited text no. 58
    
59.
Matsuki M, Adachi Y, Ozawa Y, Kimura T, Hoshi T, Okamoto K, et al. Targeting of tumor growth and angiogenesis underlies the enhanced antitumor activity of lenvatinib in combination with everolimus. Cancer Sci 2017;108:763-71.  Back to cited text no. 59
    
60.
Pippa LF, Oliveira ML, Rocha A, de Andrade JM, Lanchote VL. Total, renal and hepatic clearances of doxorubicin and formation clearance of doxorubicinol in patients with breast cancer: Estimation of doxorubicin hepatic extraction ratio. J Pharm Biomed Anal 2020;185:113231.  Back to cited text no. 60
    
61.
Johnson-Arbor K, Dubey R. Doxorubicin. StatPearls. Treasure Island (FL): StatPearls Publishing, Copyright; 2020.  Back to cited text no. 61
    
62.
Torne SJ, Ansari KA, Vavia PR, Trotta F, Cavalli R. Enhanced oral paclitaxel bioavailability after administration of paclitaxel-loaded nanosponges. Drug Deliv 2010;17:419-25.  Back to cited text no. 62
    
63.
Szebeni J, Muggia FM, Alving CR. Complement activation by cremophor EL as a possible contributor to hypersensitivity to paclitaxel: An in vitro study. J Natl Cancer Inst 1998;90:300-6.  Back to cited text no. 63
    
64.
Wang D, Wang Y, Zhao G, Zhuang J, Wu W. Improving systemic circulation of paclitaxel nanocrystals by surface hybridization of DSPE-PEG2000. Colloids Surf B Biointerfaces 2019;182:110337.  Back to cited text no. 64
    
65.
Horwitz SB. Taxol (paclitaxel): Mechanisms of action. Ann Oncol 1994;5 Suppl 6:S3-6.  Back to cited text no. 65
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed166    
    Printed36    
    Emailed0    
    PDF Downloaded9    
    Comments [Add]    

Recommend this journal