Effect of pharmacodynamical interaction between nutlin-3a and aspirin in the activation of p53
Muhammad Suleman Awan a,⇑, Maria Aslam a, Muwahida Liaquat a,⇑, A.I. Bhatti b, Afrose Liaquat c
a Department of Electrical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
b CASPR, Department of Electronic Engineering, Capital University of Science and Technology, Islamabad, Pakistan
c Department of Biochemistry, Shifa College of Medicine, Shifa Tamer-e-Millat University, Islamabad, Pakistan

a r t i c l e i n f o

Article history:
Received 12 August 2020
Revised 22 February 2021
Accepted 19 March 2021
Available online 29 March 2021

Keywords:
p53 protein Mdm2 protein DDIs
Pharmacodynamical interaction PID
a b s t r a c t

Background and objective: p53, an anti-tumour protein, is significantly inactivated in most tumours. A small molecule of nutlin-3a is used to activate its function by repressing (Mouse double minute 2 homo- log) Mdm2 protein which inhibits its activity. In cancer patients, a high risk of drug-drug interactions (DDIs) is observed owing to their multi-dosing prescriptions, which may lead them to harmful effects. In the presented work, we have aimed to investigate the effect of pharmacodynamical interaction between two anti-cancer drugs, nutlin-3a and aspirin in the activation of p53 protein.
Methods: We have adapted control system techniques and designed a Proportional-Integral-Derivative (PID) controller. This controller is used to activate p53 protein. A drug interaction parameter is used to incorporate the effect of both drugs. Extensive simulation is performed using two different doses of aspirin, i.e. a low and a high dose of aspirin.
Results: The result shows no harmful effects of pharmacodynamical interaction when a low dose is administered along with nutlin-3a. When a high dose of aspirin is administered it acts as input distur- bance and leads to undesirable over-expression of p53 protein. This can further harm other growth cells, thus inducing harmful effects. A comparative analysis is also tabulated with different dosing regimens which shows that a combination of nutlin-3a and a low dose of aspirin provides better results than a high dose of aspirin.
Conclusion: Overall, the work provides an insight to the activation of p53 protein in cancer patients under the presence of pharmacodynamical interaction and might contribute to the effective management of cancer patients.

© 2021 Elsevier Ltd. All rights reserved.

⦁ Introduction

Cancer is a well-known disease responsible for causing deaths, which essentially develops as a result of uncontrolled cell division. One of its causes is the loss of activation of tumor suppressor pro- tein, p53. The p53 protein, is therefore, becoming the main target for research in cancer due to its significant role in the suppression of cancer cells [1]. Under normal conditions, p53 protein remains at low level. Stress conditions like UV-radiations, hypoxia, and cel- lular stress damages the DNA of the cell. The tumor suppressor protein, p53, activates and responds to such stress conditions, thus initiating a repair mechanism by inducing cell cycle arrest, DNA

⇑ Corresponding authors.
E-mail addresses: [email protected] (M.S. Awan), muwahida@ee. ceme.edu.pk (M. Liaquat).
repair mechanism, and apoptosis [2]. That is why p53 protein is known as the Guardian of genome [3].
Mdm2, which is a negative regulator of p53, inhibits the activity of p53 protein [4]. When the DNA is damaged, an increase in the level of Mdm2 is observed which is stimulated by p53 protein [5]. Hence, a feedback loop is constituted between Mdm2 and p53 proteins in mutual regulation. Mdm2 protein causes the degradation of activity of p53 protein by ubiquitination process, either by mono- or poly-ubiquitination and serves as E3 ligase [6]. In most tumours, the hyperactivity of Mdm2 protein is respon- sible for increasing inactivation of p53 protein [7]. This interaction between the proteins lead to the rapid production of cancerous cells. Therefore, negative regulation of this protein interaction can restore and activate the proper function of p53 protein against tumour growth [8].
The tumour suppressor protein, p53 interacts with Mdm2 pro- tein at its desired binding site. The molecular structure of p53 pro-

https://doi.org/10.1016/j.jtbi.2021.110696

0022-5193/© 2021 Elsevier Ltd. All rights reserved.

tein shows that the binding pattern of both the proteins can be simulated using numerous small molecules. This molecular bind- ing inhibits the interaction, in the form of complex, between p53 and Mdm2 protein which further leads to the elevated levels of p53 protein [8]. Nutlin-3a is one such drug which interferes and inactivates this protein–protein interaction, thus activating p53 protein. It binds to Mdm2 protein by occupying p53 binding pocket present on Mdm2 protein. As a result, the complex between Mdm2 and p53 cannot be formed [9]. Hence, due to such binding capacity nutlin-3a can be used to bind to similar other proteins leading to functional apoptosis [10].
~
To investigate the protein interaction of p53 pathway, its dynamic response is considered. Different models were developed which focus on generating dynamic response [11]. A model devel- oped in [12], is used to generate oscillations using a negative feed- back loop between p53 and Mdm2. Under the presence of stress conditions, the loop between p53 and Mdm2 exhibit either an oscillatory or a sustained response [13]. In case of oscillatory response, the DNA damage is not extensive and is repairable. Hence, p53 undergoes a DNA repair mechanism by a process called cell cycle arrest. The extent to which the DNA is damaged is pro- portional to the frequency of these oscillations. After every 6 h, the status of DNA is monitored with the help of these oscillations. When the DNA is repaired, the oscillations die out and the cell divi- sion resumes [14]. When the DNA is extensively damaged, p53 shows a sustained response. This means that the damage is irreparable which leads to a process called apoptosis, i.e. cell death. This process stops the further division of damaged cells which has the tendency to convert into cancerous cells.
Drug-drug interactions is a multi-dosage condition in which two or more drugs interact with each other, which can further have impacts on the given patient. The presence of such interaction in a given patient plays a significant role while designing a dosing strategy, since this interaction between the drugs alters their molecular characteristics in such a way that the combined effect of drugs on the target may be more or less [15]. In vivo DDIs are divided into two categories: pharmacokinetics and pharmacody- namical interaction. The pharmacokinetics interaction investigates the aspects of pharmacokinetics properties of a particular drug [16]. The pharmacodynamical interaction is concerned with increased or decreased effects of a drug which occur as a result of a combination of two or more drugs in a given patient [16]. Patients treated for oncology are prescribed to have several drugs and may not be routinely monitored for interactions between such drugs which can initiates a high risk of interaction in them [17]. In literature, different cases of DDIs are discussed which ultimately lead to adverse effects [18–20].
Aspirin is an anti-inflammatory drug that is use to prevent inflammatory conditions. Formerly, it has been used for the patients suffering from heart diseases [21]. However, recently aspirin has been found to have anti-tumour activity against various tumours and is used to prevent them [22–23]. Aspirin induces its tumour prevention activity in different cancers such as colorectal cancer [24], colon cancer [25], lung cancer [26], breast cancer [27], etc. The anti-tumour mechanism of aspirin shows that it works as a Cyclooxygenase enzyme, COX-2, inhibitor [28]. This inhibition of COX-2 enzyme suppresses the formation of cancer cells and lead towards the successful apoptosis function [29]. The over-expression of COX-2 enzyme is observed and responsible for tumours [30,31]. This is because, an increased COX-2 activity down-regulates the transcriptional activity of p53 protein [32]. However, the use of aspirin against cancer or as a COX-2 inhibitor is dose dependent as it can significantly induce adverse effects and toxicity. For example, a high dose of aspirin may often lead to aspirin poisoning resulting in morbidity and mortality [33]. There-
fore, to counter the clinical effects of aspirin a proper (low) dosage should be administrated [34].
Now-a-days, In-silico models are used to schedule drug dosage using control systems theory for different diseases including blood pressure [35], anesthesia [36], Parkinson’s diseases [37], HIV can- cer treatment [38–40]. So far, different control system methods have been used in order to activate the p53 protein, but no work has been done in order to observe the adverse drug effects occur- ring as a result of common drug interactions among cancer patients as per our knowledge. This issue of DDIs in the activation and proper functioning of p53 protein is addressed in this work by the application of a Proportional-Integral-Derivative (PID) controller.
The remaining part of the paper is organized as follows: Sec- tion 2 provides the materials and the methods. Section 3 discusses the results on the basis of simulation. Finally, section 4 concludes the paper.

⦁ Materials and methods

⦁ p-53 pathway mathematical model

The mathematical model of p53 pathway presented in [38] investigated the interaction between Mdm2 and p53 in the form of negative feedback loop. The model allows to design a control- oriented drug dosage strategy. This interaction between Mdm2 and p53 is depicted in Fig. 1. The figure illustrates the regulation of p53 by Mdm2 protein with r being production rate of p53. The regulation of p53 is done at two main levels, i.e. its degradation and its activity as transcription factor. The parameters kt and ktl describes the transcription and the subsequent translation of the mdm2 gene to the Mdm2 protein, respectively. Whereas, d and a is the Mdm2 dependent and Mdm2 independent rates responsible for the degradation of p53, respectively. b is the spontaneous degradation rate of Mdm2-mRNA. The parameters kf and kb are the rates of p53-Mdm2 complex formation and breakup complex, respectively. However, the parameter c defines the degradation rate for Mdm2. This model is based on four Ordinary Differential Equations (ODE) given by:
dp/dt = r — ap — kf pm + kbc + cc ð1Þ
dmm/dt = kt p2 — bmm ð2Þ
dm/dt = ktl mm — kf pm + kb c + dc — cm — ka3 d(t)m ð3Þ
dc/dt = kf pm — kb c — dc — cc ð4Þ
The model considers four different concentrations described in the above equations as: p53, (p); Mdm2, (m); Mdm2 mRNA, (mm); and the p53-Mdm2 complex, (c). This model includes three biolog-

Fig. 1. p53 pathway schematic representation.

ical processes, i.e. the transcription of Mdm2 protein, subsequent translation to Mdm2 protein, and the p53-Mdm2 complex forma- tion. These processes are used in the regulation of p53 protein [38]. Eq. (2) describes the transcription of Mdm2 protein in which it depends in a quadratic way on the dynamics of p53, i.e. it estab- lishes a proportional relation with p53 dynamics. Consequently, an increase in the level of p53 increases the transcription of Mdm2. This process leads to the subsequent translation ktl to Mdm2 pro- tein for regulating p53. The model focuses on the protein level of Mdm2, therefore, the rate of translation ktl is proportional to the dynamics of Mdm2 as represented by the Eq. (3). Activation of md- m2 is significantly responsible for the regulation of suppressor pro- tein, p53, thereby providing a p53-Mdm2 complex formation in the form of negative-feedback loop. Where, a sink term, -ka3 d(t) m, is used in Eq. (3) to induce control input to activate p53. The parameter c used in the dynamics of p53 protein regulates its func- tional activity. The values of these parameters are given in the Table 1.
The pharmacokinetics data in mice is explored in [9], for oral delivery of nutlin-3a in order to investigate the effects. The equa- tion for pharmacokinetics effects of nutlin-3a is as under:
dNtot/dt = Poral d(t) — d2 N(Ntot), Ntot(to) = 0 ð5Þ
where Ntot denotes the total concentration of nutlin-3a, i.e. the protein-bounded and free nutlin-3a, qoral denotes the conversion from mg Kg—1 to moles, d(t) denotes the rate of drug dosage and t0 is the initial time of drug deliver [9]. The term – d2 N(Ntot) describes the elimination rate of free nutlin-3a which is unable to bind to the Mdm2 protein and does no useful work in order to reduce its hyper-activity.

⦁ Drug dosage design

In our research, we aim to design a dosing strategy for regulat- ing the concentration of p53 and Mdm2 protein to the desired value under the presence of pharmacodynamical interactions using control-oriented method. For this purpose, we have considered the pharmacodynamical interaction between aspirin and nutlin-3a presented in [41]. To incorporate the combined effect of both drugs, a drug interaction parameter discussed in [42] is introduced in this work for the activation of p53 protein.

⦁ Generation of sustained response of the p-53 protein

A negative feedback loop is used to stimulate the production of a sustained response of p53 protein. Fig. 2 shows the block diagram representation of this negative feedback loop. To activate p53 pro- tein, the stable equilibrium points of each output state in a four dimensional state space are considered by checking the Eigen val- ues of each equilibrium point given. The corresponding Eigen values

Table 1
p53 model parameters [38].

Parameters Explanation
Definition Values
R Production rate of p53 1000
Α Mdm2 independent degradation of p53 0.1
Β Degradation rate of Mdm2 mRNA 0.6
C Mdm2 degradation 0.2
o} Mdm2 dependent degradation of p53 11
kt Transcription of Mdm2 0.03
ktl Translation of Mdm2 1.4
kb Dissociation of Mdm2-p53 7200
kf Association of Mdm2-p53 5000
kD Dissociation constant of Mdm2-p53 1.44

Fig. 2. Block diagram of model.

for each state lie in the left-half plane. The control objective is to drive the trajectories of the system towards these desired equilib- rium points given as:
x(t) = [p mm m c]T ð6Þ
x(t) = [19.648 19.303 6.475 90.73]T ð7Þ
where p and m are the desired output of plant, i.e. p53 and Mdm2 protein respectively, represented as:
y = [p m] T ð8Þ
The ultimate goal is to reduce the hyper activated concentration of Mdm2 to activate p53 protein using the nutlin-3a. Therefore, the desired level of drug nutlin-3a, nref, is generated on the basis of Mdm2 protein with a proportionality constant K1 give as:
nref = K1 × m ð9Þ
The dosage of nutlin-3a is a function of error en which is fed into a (PID) controller. The controller gains are tuned to minimize the value of error represented as:
en = nref — n ð10Þ
d(t) = Kp en + Ki ∫en + Kd den/dt ð11Þ
where n is the actual cellular concentration of nutlin-3a, en is the error, and Kp, Ki, Kd are the controller gains. Here, the integral action of (PID) controller is used to minimize the steady state error. The derivative controller is used to smooth the output response.
In this work, to study the quantitative effects of pharmacody- namical interactions, the concentration of drug aspirin is also con- sidered given as ‘asp’ in Fig. 2. This drug acts as input disturbance to the system depending upon its concentration. Therefore, in order to generate the response of the disturbance the
mathematical model of aspirin discussed in [43] is used. A new drug interaction parameter f(t) with S(t) as dosage factor is intro- duced here to incorporate the concentration of both drugs. Thus, the combined dosage is given by:
f(t) = 1 + S(t) [q(t)/1 + q(t)] ð12Þ
S(t) = d(t) + as(t) ð13Þ
q(t) = d(t) × as(t) ð14Þ
where d(t) and as(t) is the dosage of nutlin-3a and aspirin given by Eq. (11) and from the work in [44] respectively.
dm/dt = ktl mm — kf pm + kb c + dc — cm — ka3 f(t)m ð15Þ
This equation is the evolution of Eq. (3), i.e. the equation for the output state representing the Mdm2 protein.

⦁ Results and discussion

This section discusses a simulation based analysis for the phar- macodynamical interaction between aspirin and nutlin-3a. A major challenge while investigating such interaction is the concentration of the drug aspirin, which should be administrated in a given patient. In [44], a quantitative comparison between a low and a medium dose of aspirin is conducted which shows that both the doses have similar clinical results. However, a high dose of aspirin may cause potential side effects [45]. The work presented in [41] uses a low dose of aspirin as 13 mg/kg to 15 mg/kg. In order to have a deep understanding of this interaction in the activation of p53 protein, we have considered two different concentrations of aspirin, i.e. a low dose and a high dose of aspirin. Here, we have considered a low dose of aspirin as 15 mg/kg and started to increase its dose in order to reach a high dose of aspirin that could induce toxicity in the given patient. At further increase, it was observed that a 750 mg/kg dose of aspirin acts as an input distur- bance to the system thus leading towards toxicity. It is worthwhile to mention that the dose of aspirin administered act as an input disturbance to the system depending upon its concentration, i.e. a high dose of aspirin.
The effect of a high dose of aspirin on the dosage given to the patient is shown in Fig. 3. It shows that the dosage given to the patient rises up to 1850 mg/kg. This increase in the dosage is due to the high dose of aspirin which is administered in the body along with nutlin-3a. Physiologically, it also implies that the concentra- tion of dosage inducing is now the function of both drugs, i.e. aspirin and nutlin-3a given by Eq. (12). This quantitative behaviour of dosage marks the interaction between the two drugs. A compar- ative analysis has been given using a thorough simulation between different dosing regimens of aspirin and nutlin-3a in Table 2.
The response of p53 protein in the presence of interaction between nutlin-3a and a high dose of aspirin is shown in the Fig. 4. An over-expressed response is observed in the concentration of p53 protein as a result of interaction. These results suggest that the combination of a high dose of aspirin and nutlin-3a too much enhances p53 dependent apoptosis effect, i.e. the maximum peak of p53 protein which is nearly up to 280 Nano-moles (nM) as rep- resented in Table 2. This over-expression of p53 protein leads to harmful effects in healthy growth cells [46]. Thus, a high dose of aspirin acts as an input disturbance to the system. In addition to

Fig. 3. Dosage given to the patient.
it, it was also found that a combination of a low dose of aspirin and nutlin-3a shows better performance than a high dose of aspirin in terms of settling time of p53 protein shown in Table 2. The desired concentration of p53 protein in Nano-moles (nM) is achieved by the proper tuning of a PID controller.
In Fig. 5 the concentration of Mdm2 protein is under the pres- ence of drug interaction is shown. In the transient response, a downward bump is seen in the response of Mdm2 protein with a maximum peak at around 4 Nano-moles (nM). This low concentra- tion of Mdm2 is because of the negative loop which is present between Mdm2 and p53 protein for mutual regulation. Therefore, as soon as the concentration of p53 is high the concentration of Mdm2 remains low. At steady state response, Mdm2 protein track its desired equilibrium point by the proper application of controller.

Fig. 4. Concentration of p53 protein.

Fig. 5. Concentration of Mdm2 protein.

Fig. 6. Error Signal.

Table 2
Comparison of dosing regimens.

Dosing Regimen Explanation
Settling Time of p53 protein (sec) Max. Peak of p53 protein (nM) Steady State Error en
Nutlin-3a 280 200 0
Nutlin-3a + Low Aspirin Dose 350 240 1.4 e—7
Nutlin-3a + High Aspirin Dose 1000 280 (over-expressed) 0.09

Fig. 6 illustrates the response of steady state error en which is introduced as a result of occurrence of this interaction. It is observed that the value of the error increases at the transient response, which then goes negative as a result of high dose of aspirin administered along with nutlin-3a. This is because of the actual concentration of drug present in the cell as per our devel- oped model. Thus, a high dose of aspirin acts as disturbance at input. From the simulation result given in Fig. 6 it is shown that the error starts decreasing and then tracks its steady state value as represented in Table 2.

⦁ Conclusion

In this paper, we have aimed to investigate the pharmacody- namical interaction among cancer patients in the activation of p53 protein. A control-oriented drug dosage design is devised to activate p53 protein. We have considered the pharmacodynamical interaction between two anti-cancer drugs, i.e. drug aspirin and nultin-3a. Control system techniques are used and a PID controller is tuned to activate the p53 protein. A closed-loop simulation is performed to analyze the results using different doses of aspirin,
i.e. a low dose and a high dose of aspirin. It was observed that a high dose of aspirin leads to an undesirable over-expression of p53 protein, thus acting as disturbance. Furthermore, a compara- tive analysis is also tabulated between different dosing regimens which shows that a low dose of aspirin when combines with nutlin-3a leads to better results than a high dose of aspirin. Hence, it can be concluded that pharmacodynamical interaction between nutlin-3a and aspirin can lead to other undesirable harmful effects, if not controlled, in the activation of p53 protein and might con- tribute to the effective management of cancer patients.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing finan- cial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

Suzuki, K., Matsubara, H., ]. Recent advances in p53 research and cancer treatment.
J. Biomed. Biotechnol. 978312.
Ma, L., Wagner, J., et al., ]. A plausible model for the digital response of p53 to DNA damage. Proc. Natl. Acad. Sci. U.S.A. 102 (40), 14266–14271.
Lane, D.P., ]. p53, guardian of the genome. Nature 358 (6381), 15–16. https://doi. org/10.1038/358015a0.
Bond, G.L., Wenwei, H., Levine, A.J., ]. Mdm2 is a central node in the p53 pathway: 12 years and counting. Curr. Cancer Drug Targets 5 (1), 3–8.
Price, B.D., Park, S.J., ]. DNA damage increases the levels of MDM2 messenger RNA in wtp53 human cells. Cancer Res. 54 (4), 896–899.
Brooks, C.L., Wei, G.u., ]. p53 regulation by ubiquitin. FEBS Lett. 585 (18), 2803– 2809.
Zhao, Y., Yu, H., Hu, W., ]. The regulation of MDM2 oncogene and its impact on human cancers. Acta Biochim. Biophy. Sin. 46 (3), 180–189. https://doi.org/ 10.1093/abbs/gmt147.
Rew, Y., Sun, D., ]. Discovery of a small molecule MDM2 inhibitor (AMG 232) for treating cancer. J. Med. Chem. 57 (15), 6332–6341.
Puszynski, K., Gandolfi, A., d’Onofrio, A., ]. The pharmacodynamic of the p53-Mdm2 targeting drug nutlin: the role of gene-switching noise. PLoS Comput. Biol. 10, (12) e1003991.
Ji-Hyang, H. et al., 0]. Molecular mimicry-based repositioning of nutlin-3 to antiapoptotic Bcl-2 family proteins. J. Am. Chem. Soc 135 (5), 1244–1247.
Ciliberto, A., Novak, B., Tyson, J.J., 1]. Steady states and oscillations in the p53/Mdm2 network. Cell Cycle 4 (3), 488–493. https://doi.org/10.4161/cc.4.3.1548.
Bar-Or, L., Maya, R., et al., 2]. Generation of oscillations by the p53-mdm2 feedback loop: a theoretical and experimental study. Proc. Natl. Acad. Sci. U.S.A., 11250– 11255
Sun, T., Cui, J., 3]. Dynamics of P53 in response to DNA damage: mathematical modeling and perspective. Prog. Biophys. Mol. Biol. 119 (2), 175–182. https:// doi.org/10.1016/j.pbiomolbio.2015.08.017.
Teodoro Jose, G., Evans, S.K., Green, M.R., 4]. Inhibition of tumour angiogenesis by p53: a new role for The Guardian of the genome. J. Mol. Med 85, 1175–1186.
Yeh, P.J., Hegreness, M.J., Aiden, A.P., Kishony, R., 5]. Drug interactions and the evolution of antibiotic resistance. Nat. Rev. Microbiol. 7 (6), 460–466. https:// doi.org/10.1038/nrmicro2133.
Palleria, C., Di Paolo, et al., 6]. Pharmacokinetic drug-drug interaction and their implication in clinical management. J. Res. Med. Sci. 18 (7), 601–610.
van Leeuwen, R.W.F., Jansman, F.G.A., van den Bemt, P.M.L.A., de Man, F., Piran, F., Vincenten, I., Jager, A., Rijneveld, A.W., Brugma, J.D., Mathijssen, R.H.J., van Gelder, T., 7]. Drug–drug interactions in patients treated for cancer: a prospective study on clinical interventions. Ann. Oncol. 26 (5), 992–997. https://doi.org/10.1093/annonc/mdv029.
Stoll, P., Kopittke, L., 8]. Potential drug–drug interactions in hospitalized patients undergoing systemic chemotherapy: a prospective cohort study. Int J Clin Pharm 37 (3), 475–484. https://doi.org/10.1007/s11096-015-0083-6.
Umar, R.M., 9]. Drug-drug interactions between antiemetics used in cancer patients.
J. Oncol. Sci. 4 (3), 142–146. https://doi.org/10.1016/j.jons.2018.07.003.
Riechelmann, R.P., Del Giglio, A., 0]. Drug interactions in oncology: how common are they? Ann. Oncol. 20 (12), 1907–1912.
Ansa, B.E., Hoffman, et al., 1]. Aspirin use among adults with cardiovascular disease in the United States: implications for an intervention approach. J. Clin. Med. 8 (2), 264.
Ho, C., Yang, et al., 2]. Activation of p53 signalling in acetylsalicylic acid-induced apoptosis in OC2 human oral cancer cells. Eur. J. Clin. Investig. 33 (10), 875–882. Chan, A.T., Giovannucci, E.L., Meyerhardt, J.A., Schernhammer, E.S., Wu, K., Fuchs, C. S., 3]. Aspirin dose and duration of use and risk of colorectal cancer in men.
Gastroenterology 134 (1), 21–28. https://doi.org/10.1053/j.gastro.2007.09.035. Fu, J., Xu, Y., Yang, Y., Liu, Y., Ma, L., Zhang, Y., 4]. Aspirin suppresses chemoresistance and enhances antitumor activity of 5-Fu in 5-Fu-resistant
colorectal cancer by abolishing 5-Fu-induced NF-jB activation. Sci. Rep. 9 (1).

https://doi.org/10.1038/s41598-019-53276-1.

Peerji Li, Diangeng et al. Tumor-preventing activity of aspirin in multiple cancers based on bioinformatic analyses. PeerJ (6) e5667, 2018.
Mc Menamin, Ú.C., Cardwell, C.R., Hughes, C.M., Murray, L.M., 6]. Low-dose aspirin and survival from lung cancer: a population-based cohort study. BMC Cancer 15 (1). https://doi.org/10.1186/s12885-015-1910-9.
Maity, G., De, A., Das, A., Banerjee, S., Sarkar, S., Banerjee, S.K., 7]. Aspirin blocks growth of breast tumor cells and tumor-initiating cells and induces reprogramming factors of mesenchymal to epithelial transition. Lab. Invest. 95 (7), 702–717. https://doi.org/10.1038/labinvest.2015.49.
Thun, Michael J et al. Nonsteroidal anti-inflammatory drugs as anticancer agents: mechanistic, pharmacologic, and clinical issues. J. Natl. Cancer Inst. 94(4) 2002. Zhang, Xiaoqi et al. Aspirin promotes apoptosis and inhibits proliferation by blocking G0/G1 into S phase in rheumatoid arthritis fibroblast-like synoviocytes
via downregulation of JAK/STAT3 and NF-jB signaling pathway. Int. J. Mol.
Med., 42(6) 2018.
Wang, Z., Chen, J., Liu, J., 0]. COX-2 inhibitors and gastric cancer. Gastroenterol. Res.
Practice 2014, 1–7. https://doi.org/10.1155/2014/132320.
Liu, B., Qu, L., Yan, S., 1]. Cyclooxygenase-2 promotes tumor growth and suppresses tumor immunity. Cancer Cell Int 15 (1). https://doi.org/10.1186/s12935-015- 0260-7.
de Moraes, Emanuela et al. Cross-talks between cyclooxygenase-2 and tumor suppressor protein p53: Balancing life and death during inflammatory stress and carcinogenesis. Int. J. Cancer 121(5), 2007.
Dargan, P.I., Wallace, C.I., Jones, A.L., 3]. An evidence based flowchart to guide the management of acute salicylate (aspirin) overdose. Emergency Med. J.: EMJ 19 (3), 206–209.
Thun, M.J., Jacobs, E.J., Patrono, C., 4]. The role of aspirin in cancer prevention. Nat. Rev. Clin. Oncol. 9 (5), 259–267. https://doi.org/10.1038/nrclinonc.2011.199.
Shahin, M., Maka, S., 5]. PI controller based closed loop drug delivery for the long term blood pressure regulation. In: 2012 Annual IEEE India Conference (INDICON), pp. 998–1002.
Neckebroek, Martine, Smet, et al, Automated drug delivery in anesthesia, Curr.
Anesthesiol. Rep. (3), 18–26, 2012.
Perera, T., Tan, J.L., et al., 7]. Balance control systems in Parkinson’s disease and the impact of pedunculopontine area stimulation. Brain: J. Neurol. 141 (10), 3009– 3022.
Hunziker, A., Jensen, M.H., Krishna, S., 8]. Stress-specific response of the p53-Mdm2 feedback loop. BMC Syst. Biol. 4 (1), 94. https://doi.org/10.1186/1752-0509-4-
94.
Haseeb, M., Azam, S., Bhatti, A.I., Azam, R., Ullah, M., Fazal, S., 9]. On p53 revival using system oriented drug dosage design. J. Theor. Biol. 415, 53–57. https://doi. org/10.1016/j.jtbi.2016.12.008.
Azam, M.R., Fazal, S., Ullah, M., Bhatti, A.I., 0]. System-based strategies for p53 recovery. IET Syst. Biol. 12 (3), 101–107. https://doi.org/10.1049/iet- syb.2017.0025.
Miao, R., Xu, et al., 1]. Synergistic effect of nutlin-3 combined with aspirin in hepatocellular carcinoma HepG2 cells through activation of Bcl-2/Bax signaling pathway. Mol. Med. Rep. 17 (3), 3735–3743.
Chakrabarti, S., Michor, F., 2]. Pharmacokinetics and drug interactions determine optimum combination strategies in computational models of cancer evolution. Cancer Res. 77 (14), 3908–3921.

Johng, Breeana J., A mathematical model of the effect of aspirin on blood clotting, Scripps Senior Theses, 718, 2015.
Van Gijn, J., Algra, A., Kappelle, J., Koudstaal, P.J., van Latum, A., 4]. Dutch TIA Trial Study Group. A comparison of two doses of aspirin (30 mg vs. 283 mg a day) in patients after a transient ischemic attack or minor ischemic stroke. N. Engl. J. Med. 325 (18), 1261–1266.
Gaspari, F., Viganò, et al., 5]. Aspirin prolongs bleeding time in uremia by a mechanism distinct from platelet cyclooxygenase inhibition. J. Clin. Investig. 79 (6), 1788–1797.
Andrei V. Gudkov, Elena A. Komarova, Dangerous habits of a security guard: the two faces of p53 as a drug target, Human Mol. Genetics, 16, R67–R72, 2007.