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Opioid Addiction and Using Methadone as a Substitutional Treatment

Opioid addiction and using methadone as a substitutional treatment 

  1. Introduction:

1.1 opioid dependence:

Drug dependence is a serious problem facing different societies worldwide, one which is leading to great cost and creating social problems. According to recent studies, drug dependence costs the United States of America (USA) about $67 billion every year in lost work productivity, foster care, crime and many other social problems. The cost impacts of drugs on social regulations have an important role in forming public opinion that drug dependence is primarily a social issue, which needs prohibition and law enforcement, instead of a health problem, which needs prevention and treatment (McLellan et al., 2009).

Many adults have a high intake of alcohol and/or other drugs, which can lead to the point of abuse and, on occasion, a medical condition (dependence). Dependence, which is usually called addiction, is a pathological condition that is demonstrated by three to seven criteria. Tolerance and withdrawal criteria are  indicators of a neurologic adaptation that is termed physiologic dependence. Opioid dependence is one of the most common drug dependence problems (McLellan et al., 2009).

Opioids are substances that bind to the opioid receptors in the central and peripheral nervous system to produce analgesic effect. Opioids are physioactive materials extracted from the opium poppy or their synthetic analogues which have similar impacts. These opioids contain natural opiates, such as morphine and opium, and natural or synthetic derivatives, such as buprenorphine, heroin, oxycodone and methadone. Opioids can be consumed through the inhalation of the fumes from heating or injection. Euphoria, which is related to the recreational use, is the result of using these drugs. If opioids are used regularly, this will lead to opioid addiction. Opioid addiction is a medical condition represented in the inability to cease opioids intake (Mattick et al., 2014).

Opioids have the possibility to cause dependence, which is characterised through the continuous need to take opioids, reduction in controlling the usage of opioids, and continuous usage of opioids despite the deleterious consequences. The usage of opioids takes on higher priority than any other obligations and activities, enhanced tolerance is manifested and can be physical impairment when opioids intake ceases. Addiction to prescribed opioids involves iatrogenic dependence, which can lead to theft of prescription  opioids from medical facilities, pharmacies, patients, and the manufacturing and distribution chains (WHO, 2017a).

The pharmacological effects of opioids can cause respiratory depression and death due to taking opioids in high doses. Approximately, 69 000 people die every year because of opioid overdose. Around 15 million people worldwide are suffering from opiate dependence. Most people who are addicted to opioids use illegally cultivated and manufactured heroin and, increasingly, people are using prescription opioids (WHO, 2017a).

Figure (1): Overdose opioid deaths in 2014 by ages per 100,000 people (Frontline, 2017).

In 2007, it was shown that opiate dependence  mainly occurs in Afghanistan and the neighbouring countries due to the global opium production there, which accounts for approximately 93%. Dr. Thomas Pietschmann of the United Nations Office of Drugs and Crime (UNODC) Policy Analysis and Research Branch said that: “Opioid addiction is rising in countries surrounding Afghanistan involving the Islamic Republic of Iran, India, countries along the east coast of Africa and countries in central Asia. On the other hand, opioid dependence is constant or even decreasing in most of Europe countries, North America, and various parts of south-east and east Asia” (WHO, 2017b).

Patralekha Chatterjee said that: “There is no miracle to solve the dependence on opiates such as heroin.” The World Health Organization’s (WHO) new guidelines shows that substitution therapies such as methadone are the most effective, suitable and promising solution to reduce the opioids dependence problem, even after 40 years, but gaining access to these therapies is a universal problem. Globally, it was thought that fewer than 650 000 people are receiving the substitutional therapy for opiate addiction, and fewer than 10% of these people need this therapy. This substitutional therapy is a psychoactive material, which  involves medically supervised administration and is similar to that causing addiction (WHO, 2017b).

Around 40-50% of opioid users are receiving substitutional therapy in countries where this therapy is available. Most of the Eastern Europe and Asia countries, especially the Russian Federation, China and India, are not receiving their actual need for treatment (WHO, 2017b).

1.2 methadone as a substitutional therapy:

Methadone and buprenorphine are the most two common compounds used as a substitutional therapy with people suffering from opiate dependence. These two drugs are non-toxic agents and work on reducing the effect of any additional/other opioid use. In 2004, this long-term therapy was confirmed by UNODC, WHO and UNAIDS (Joint United Nations Programme on HIV/AIDS) in a joint position document.  A clear link was demonstrated between the substitutional treatment for opiate addiction and the massive reductions in illegal usage, deaths resulting from overdose, criminal activities and attitude leading to high risk of HIV transmission (WHO, 2017b).

However, several factors hinder the expansion of methadone/ buprenorphine treatment for opiate addicts, including social, political and cultural factors. Methadone or/and buprenorphine, which are maintenance treatments, are either illicit or unavailable in many countries. In the Russian Federation, “using methadone or buprenorphine as a maintenance treatment is prohibited by law, although it has one of the highest rates of opioids usage. Also, in the Russian Federation, the treatment for opiate abuse is abstinence oriented”, says Dr. Evgeny Krupitsky (WHO, 2017b).

The more strict the rules in controlling measures of methadone by governments, the easier to get access to the narcotic as a substitutional therapy. Pavel Pachta stated that: “The governments of some countries, where they allow the usage of methadone for scientific or/and medical reasons may face problems in arriving supplies of methadone (e.g. importation of methadone), these problems are connected to problems their national competent authorities have with the application of control measures for opioids” (WHO, 2017b).

In spite of these huge challenges in accessing methadone for maintenance treatment, there are promising signs. For example, China is enhancing its methadone substitutional therapy programme for heroin addicts (WHO, 2017b).

Moreover, “In China, the first clinic for methadone maintenance therapy was piloted in 2004”, says Dr. Li Jianhua. Then, by the end of 2007, there were 503 clinics for methadone substitutional treatment in 23 different provinces. In these clinics, around 60 000 heroin-addicted patients were receiving maintenance treatment up to the end of 2007. It became a promising and effective treatment approach for heroin-addicted patients and a way to prevent the spread of HIV/AIDS (WHO, 2017b).

1.3 methadone:

Methadone is a cost-effective narcotic agonist that has a great impact in treating cancer pain and chronic and acute neuropathic. It is widely used, not only as a first-line analgesic, but also in opioid rotation strategies (Kharasch et al., 2011). In addition, this drug is the cornerstone in treating opioid and opiate dependence, avoiding withdrawal and illegal drug usage. Methadone is also a vital public health strategy for the risk lowering of HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome) (Connock et al., 2007; Kharaschet al., 2011).

Since 1965, methadone has been formally produced and has become increasingly used as a substitutional therapy. Rac-methadone (6-dimethylamino-4, 4-diphenyl-3-heptanone) is a chiral agent, which has two enantiomers (R and S-methadone). R-methadone has higher affinity than S-methadone by 10 folds at δ and µ opioid receptors. Also, R-methadone has up to 50 times the strength of analgesic effect of S-methadone in animal and human models of antinociception. While the R-methadone prevents the appearance of opioid withdrawal symptoms, the S-methadone is ineffective. Although, the eudismic ratio is high, in most countries methadone is used as the racemate (Foster et al., 2001).

1.4 methadone metabolism:

Methadone is potent drug, one which is highly effective and has perfect absorption from the gastrointestinal tract (GIT), very low cost, no known active metabolites, and has two different routes of administration -oral and intravenous. Methadone is mainly cleared by the liver with minor urinary elimination of unchanged drug. The essential metabolic pathway is N-demethylation to 2-ethyl-1, 5-dimethyl- 3, 3-diphenylpyrrolidine (EDDP), which is pharmacologically inactive, by cytochrome P450 (CYP) enzymes. Furthermore, methadone drug interactions exist with CYP inhibitors. such as antiretroviral drugs, antifungal and antidepressant agents, and CYP inducers such as phenytoin and rifampicin (Clark, 2008; Totah et al., 2008).

The CYP enzyme system plays an important role in the first phase metabolism of methadone. There are approximately 50 hepatic CYP enzymes expressed in humans. In addition, the predominant metabolising isoform depends on which isomer of a racemic mixture of drug is being followed. In methadone metabolism different isoforms have been embroiled, involving CYP2B6, CYP3A4, CYP2D6, CYP1A2, CYP2C8, CYP2C9 and CYP2C19 (Clark, 2008; Totah et al., 2008).

Studies have shown that CYP2B6 has a major impact on methadone metabolism in humans, especially the S-methadone enantiomer. Thus, CYP2B6  controls the overall plasma levels and the ratio of plasma methadone isomers (Clark, 2008; Totah et al., 2008).

Moreover, genetic factors have a great impact on the metabolism process through the CYP enzymes, especially CYP3A4 and CYP2B6 isoforms, which play an important role in the hepatic metabolism of methadone. These two genes are polymorphic in humans and this nature of these genes has been confirmed to influence the production of metabolites, clearance and the prospect to reach the clinical endpoints for different drugs, involving methadone. For instance, in 209 patients in methadone maintenance, it was attempted to associate CYP2C19, CYP2C9 and CYP2B6 polymorphs with plasma levels of methadone. A positive association was found just for CYP2B6 variants (Clark, 2008).

Methadone maintenance concentration can be predictable through its therapeutic ranges, which is confounded by the massive and unpredictable difference of methadone pharmacokinetics profiles, especially clearance, long elimination half-life (t1/2), and capability to drug interactions between various individuals. The prospective consequences are unwanted adverse effects or inadequate treatment, such as respiratory depression or withdrawal (Totah et al., 2008).

1.5 methadone absorption:

Methadone is a liposoluble drug, which is a racemic mixture of S and R-methadone, as was mentioned before, and has a pKa of 9.2 (Garrido and Trocóniz, 2000; Nilsson et al., 1982). The absorption of methadone is fast and nearly complete after it is administered orally as a solution or tablets. One study has shown that the mean time needed to reach the peak plasma methadone concentration was 2.5h in solution (Wolff et al., 1993; Garrido and Trocóniz, 2000). Other study reported that it needs 3h in tablets (Nilsson et al., 1982). The oral bioavailability of methadone has a range between 0.67 and 0.95, which is considered to be high bioavailability (Garrido and Trocóniz, 2000).

1.6 methadone distribution:

In addition, methadone is highly distributed in the tissues as a result of being a lipophilic drug (Säwe, 1986). It has been shown that there are no studies published transacting with the physiological model in humans for methadone distribution. Nevertheless, Gabrielsson et al. (1985)  worked on pregnant rats and showed that methadone was distributed to the liver, kidney, gut, brain, lung and muscle with tissue to plasma partition coefficients of 44.2, 76.6, 4.6, 37.2, 156.3, and 14.7, respectively. These results confirm the high volumes of distribution reported in humans. In spite of the huge differences by different authors in the mean volume of distribution at the steady-state (Vss), the values are considerably higher than the factual physiological volume, in most cases, which indicates the tissue binding command over binding to plasma proteins (Garrido and Trocóniz, 2000).

Furthermore, methadone has a high tendency to bind to the plasma proteins and its percentage is approximately 86% and this s nearly the same in other species, such as rats. Due to methadone’s basic properties, it binds mainly to α1 -acid glycoprotein (AAG), which is an acute-phase reactant protein. AAG have shown differentiation in  plasma binding depending on pathological or physiological conditions. During stress condition, it was recognised that AAG levels rise significantly. This increase in AAG levels is the main reason of lowering free fraction (fu) for methadone in plasma in opioid addicts and cancer therapy in comparison with healthy people. Rapid drug intake (i.e., bolus) resulted in decrease fu in the early period after drug administration by a rise in bound plasma drug concentration (Cp), because Vss is proportional to fu, whilst the unbound plasma drug concentration (Cu) will remain constant. The decrease in fu will not adjust the maximum observed response,  assuming that Cu is the pharmacological active concentration. The tissue distribution rate depends on Cp, but not on Cu; however, this rate will be increased because of the lipophilicity of the drug (Garrido and Trocóniz, 2000).

1.7 methadone elimination:

Methadone metabolises by the kidneys and excretes through the liver (hepatic metabolism and renal excretion). Due to its pKa of 9.2 and lipophilic properties, alterations in the pH values in the urinary tract are the major evidence of methadone elimination. When the pH of urinary tract is above 6, renal clearance is just about 4% of the total methadone elimination. Whilst, when pH value is below 6, the unchanged drug eliminated through the kidney can rise to 30% of the total administered dose. Also, when the urinary pH is integrated in the model as a variable, the estimation of the differences between people in methadone clearance decreases by 27% (Garrido and Trocóniz, 2000).

Methadone is deemed to be a low extraction ratio drug or  restrictive cleared regarding to its hepatic elimination. Mean clearance of methadone has been estimated of 3.1 and 1.5 ml/min/kg in opiate addicts and in patients suffering from chronic pain, respectively. In the use of 0.75 for the blood-to-plasma ratio and the presumption of a blood flow rate of 1500 ml/min, it has been shown that the hepatic extraction ratio is 0.08 and 0.16 for patients with chronic pain and opiate addicts, respectively. These excretion characteristics of methadone explain its high bioavailability after it is administered orally and have consequences that are relevant to the interindividual variation in clearance. Clearance depends on the fu and the intrinsic clearance (Clint), which is the intrinsic enzymatic activity of the liver. Moreover, changes in liver perfusion, Clint and fu will not affect the bioavailability of methadone. It has been shown previously that fu and AAG levels are adjusted in opiate addicts and cancer patients. Thus, plasma protein binding is considered to be a potential factor responsible for the differences between the individuals in clearance (Garrido and Trocóniz, 2000).

In addition, in  study of the effects of changes of fu and the kinetics of a drug in the steady-state condition, the steady-state concentration (Cpss) of a drug depends on the clearance (Cl) and the rate of administration (k0), which can be determined by the following equation: Cpss= k0/Cl. For methadone, which  has low extraction ratio, the reduction in fu will reduce the Cl, and, as a result, Cpss will be increased proportionally. Whereas the unbound steady-state concentration (Cuss) of methadone will remain fixed because of this equation: Cuss= fu*Cpss. The time to reach steady-state for both Cp and Cu is not adjusted, because both V and Cl will be reduced proportionally with fu (Garrido and Trocóniz, 2000).

The biotransformation of methadone is mediated mainly through CYP3A4. Also, CYP2C9 and CYP2C19 take part in methadone biotransformation. but to a much lesser extent than CYP3A4 (Foster et al., 2001). The variations in CYP3A4 expression between individuals are the main reason of the variation of clearance between different people. Moreover, although methadone is able to modify various CYP2D6 substrates, it has been shown that this enzyme is not catalysing methadone biotransformation (Garrido and Trocóniz, 2000).

It has been shown that CYP3A4 is an inducible enzyme, which makes the perception and prediction of methadone clearance of individuals more difficult. In a previous study, it was reported that the total clearance in the first dose of methadone is higher by 3.5-fold that in the steady-state doses in opioid users. This finding is due to the ability of methadone to motivate its own metabolism. According to the previous results, time is considered to be a variable from the beginning of the treatment to be involved in clearance of methadone. Other variable researchers should consider are the concomitant medicines with drugs such as phenytoin, rifampicin, carbamazepine, spironolactone, verapamil, amitriptyline, zidovudine, barbiturates and diethylstilbestrol. It has been reported that these drugs induce the elimination kinetics of methadone. In addition, in vitro studies  showed that fluoxetine inhibited the formulation of methadone main metabolite by about 50%. Omeprazole and carbamazepine have similar results to fluoxetine. Also, it has been shown that CYP3A4 inducers, such as rifampicin, are able to up-regulate the production of AAG, enhancing the plasma binding of basic drugs. The phenomenon of up-regulation has been proposed to be involved in the slight rise in AAG, which was seen after a lengthy period of methadone treatment (Garrido and Trocóniz, 2000).

Although, the characteristics of methadone distribution in laboratory animals and humans seems the same, for its elimination it is not the case. It has been reported that methadone elimination is nonrestrictive in rats with mean clearance of 0.1 l/min/kg. Variation in the elimination process between species is probably because of the differences in the CYP isoforms expressions in the metabolism of methadone (Garrido and Trocóniz, 2000).

1.8 stereoselective pharmacokinetics:

It has been reported that the volume of distribution of S-methadone and R-methadone in patients with chronic pain was 289.11 and 496.61, respectively. Also, it was reported that the clearance of S-methadone and R-methadone was 0.129 and 0.158 l/min, respectively (Kristensen et al., 1996). Other study has shown that, in Beagle dogs, the clearance for R-methadone is slightly higher than for S-methadone: 0.357 and 0.316 l/min, respectively (Schmidt et al., 1994). Also, it has been shown that there is a lack of stereoselective metabolism in human microsomes (Foster et al., 2001). These results explain the reason of the clearance variations between the enantiomers, which is because of the differences in plasma protein binding. It was reported that fu for S-methadone and R-methadone was 0.10 and 0.13, respectively. This difference, even it is small, can demonstrate the variation in clearance between the enantiomers. The previous discussed results show the kinetics differences between the two enantiomers; it can be supposed that changes in metabolism, protein binding, body composition and other demographic data will influence the two enantiomers due to the estimation of volume of distribution and clearance. In addition, there are no changes in the bioavailability and the lag time between the two enantiomers regarding the absorption process (Garrido and Trocóniz, 2000).

  1. methadone dosing:

During methadone intake, patients should be advised to stop consuming sedatives or opioids (involving alcohol) for 24 hours before starting methadone therapy. The starting dose should be administered in the morning. The initial dose will depend on the usage of any other drugs, such as, sedatives, and on the severity of opioid addiction (, 2008). The induction dose of methadone requires caution. Different risk factors have been realised, such as the quantity of initial methadone dose, the general health of patients and concomitant usage of other drugs (Humeniuk et al., 2000).

The maximum initial dose of methadone should be 30mg. Patients should be monitored after four  hours of taking the first dose for signs of toxicity or withdrawal so as to know the alteration of methadone dose in the first week of the therapy. In the first week of treatment, the maximum daily dose of methadone should not exceed 40mg. Increases  in dosing methadone should be every four  days because has a long half-life, so it takes five days for a change in dose to show its full influence (, 2008).

Figure (2): The induction dose of methadone maintenance.

Also, patients should be monitored after three to four days of the starting dose. In this stage, it will be clear whether the initial dose of methadone needs to be increased or remain constant. For most patients who are suffering from withdrawal symptoms, the dose can be increased by not more than 10mg. Withdrawal symptoms shown only prior the intake of the next dose  denote that the dose can be increased by less (, 2008).

Administration of a higher-dose of methadone, more than 60mg daily, is related to greater repression of illegal opiate usage and better retention in therapy. Patients who are receiving methadone should be given a dose between 60-120mg, by the guidance of clinical assessment. Usually, this takes a few weeks (, 2008).

In the guidance of changing a dose, there are two important features of clinical assessment, which are  represented by the usage of illicit opioids and the presence of signs and symptoms of withdrawal or toxicity (, 2008).

The increase in methadone dosing should not be stopped before the disappearance of withdrawal symptoms, unless there are signs of toxicity, such as pinpoint pupils or drowsiness, and usage of illegal opioids less than weekly. Methadone dose should be increased by a maximum 10mg with a gap of four days at least separating each dose increase to be aware of overdose problem (, 2008).

During pregnancy, the dose of methadone should be increased due to the fluid retention, which will increase the volume of distribution. Due to this reason and because of other metabolic changes, the dosage of methadone should be increased by 5-10mg or more in the subsequent half of the pregnancy period. Also, in some cases. split dosing (twice daily) may be required. Postnatal methadone dosage should be reduced (, 2008).

  1. the effect of different populations on the pharmacokinetics of drugs (i.e. methadone):

Different populations may affect the pharmacokinetics of different drugs. including methadone, because of various physiological changes. This project will discuss three different populations and their effect on kinetics profiles.

1.9.1 the influence of geriatrics on a drug pharmacokinetics:

Nowadays, the population in modern societies has become older. In 2025, it is predicted that the elderly will represent 20% of the population. People aged 65 and over are considered as elderly people (Aymanns et al., 2010). Approximately 90% of geriatrics take at least one drug and most of them take two or more medications. Changes in pharmacokinetic parameters, which are absorption, distribution, metabolism and excretion, may occur with aging (Howland, 2009).

Volume of distribution of any drug depends on both total body water and body mass, fat and muscle, which are reduced with ageing.  Also, in geriatrics, the relative proportion of lean muscle mass to total body fat is decreased. The majority of psychotropic drugs are stored in fat tissue, except lithium. Due to this reason, many drugs have the capability to accumulate in elderly patients more than in healthy patients. The excretion of these drugs from fatty tissues is slow, which leads to prolonged effects. Hence, the slow release from fat stores of already administered drugs together with the combined effects of ongoing administration  can end up with higher and higher concentrations  in the central nervous system (CNS) over time. This may cause overdosing in elderly patient, which will cause toxicity. In the case of drugs that are distributed in water, such as lithium, higher concentrations of the drug will be needed for specific dose as the total body water reduces. Because of both the susceptibility of dehydration in elderly patients and the reduction of total body water, the risk of side effects with the usage of water-soluble drugs including lithium is increased in geriatrics (Howland, 2009).

Usually all drugs enter the systemic circulation and most of them will bind reversibly to different plasma proteins, regardless to its route of administration. Different psychotropic drugs are highly protein-bound, except lithium. In protein-bound drugs, a higher  fraction of binding occurs in a reversible state of equilibrium with the smaller unbound fraction “free-floating”, which is known by free fraction. Only these free fractions of the drug cause its side effects and pharmacological therapeutics, because of the ability to cross the blood-brain barrier into the central nervous system (CNS) or  the ability to bind to other organs in the body. In addition, only the free fractions metabolise through the liver and are cleared by the kidneys (Howland, 2009).

With age, the plasma proteins tend to reduce and even more with elderly patients who are undernourished or debilitated. This results in there existing a great proportion of protein-bound drugs as a free fraction in older patients. This may involve in the increased risk of side effects in CNS or other body systems. Administration of multiple drugs, which bind to the same plasma protein,  will lead to displacement of the protein-bound fraction, which  will cause increase in concentrations of the free fraction. Also, this will result in more adverse effects in geriatrics (Howland, 2009).

Most of the psychotropic drugs undergo metabolism through the liver, with a few exceptions such as lithium and gabapentin. Ageing reduces the hepatic blood flow and some liver enzymes activity, which will lead to reduction in metabolism of various drugs in elderly patients. Metabolism in the liver has two main pathways involving the conjugation processes and oxidative processes. Conjugation is not affected by age as are the oxidative processes, which decrease by ageing. The majority of oxidative processes are mediated by CYP enzymes. These CYP enzymes have the capability to metabolise about 60% of the prescribed drugs. Different CYP enzymes occur in the liver. Ageing does not affect  the activity of all CYP enzymes. For instance, the activity of CYP2D6 is not affected by ageing, while the activity of CYP3A4 and CYP2C19 is reduced with ageing. Thus, changes in metabolising any drug occurring with ageing are dependent on the particular enzymes involved. In addition, drugs bind to different CYP enzymes, which can induce or inhibit the enzyme activity. Due to the multiple medications of  elderly patients, more side effects are expected, not only because of the changes in metabolism with ageing, but also because of the drug interaction of the multiple medications (Howland, 2009).

In addition, the excretion of most psychotropic drugs and their metabolites is through the kidneys. Renal function reduces by ageing, which  is particularly important to the usage of lithium in geriatrics. Renal function is important for various drugs because they are cleared by the kidneys after their metabolism in the liver. Reduction in renal clearance will lead to increase in the concentration of the drugs and their active metabolites, which increases the risk of the toxic or adverse effects (Howland, 2009).

1.9.2 the effect of pregnancy on methadone pharmacokinetics:

During the pregnancy period, many physiological changes occur. Although, these changes starts in the first trimester of pregnancy, they become more obvious during the third trimester. The physiological changes alter the absorption, distribution, metabolism and elimination of drugs. Also, the majority of drugs get access to the feto-placental unit (Dawes and Chowienczyk, 2001).

During pregnancy, small intestine motility and gastric emptying are decreased because of the increase in progesterone. This may result in elevation in Tmax and decrease in Cmax values, although the influence on the total bioavailability may be comparatively slight. Elevation in mucus production and reduction in H+ secretion lead to increase the gastric pH, which will increase the ionisation of weak acids that tend to decrease their absorption more that the absorption of weak bases. With repeated dosing, these effects may not be important. Moreover, they may decrease the efficacy of a single dose of an oral drug, such as anti-emetic or analgesic in which Cmax and Tmax are important. Nausea and vomiting during pregnancy may reduce the amount of drug obtainable for absorption. In case of once daily dosing, if the nausea is minimised in the evening, its influence on absorption can be reduced by delaying the dose intake to the evening. Absorption of the inhaled drugs may be elevated because of the increase in tidal volume and cardiac output enhancing alveolar uptake. For instance, the dose of volatile anaesthetic agents, such as halothane, should be decreased during pregnancy. Absorption of drugs administered intramuscularly is increased, usually by enhancing tissue perfusion secondary to vasodilation (Dawes and Chowienczyk, 2001).

In the pregnancy period, there will be amplification in both extravascular (uterus, breasts, peripheral oedema) and intravascular (plasma volume) water content. Therefore, the total body water rises by approximately 8 litres, generating a larger space within the distribution of hydrophilic drugs, i.e. elevating Vd. Due to this apparent dilution, f many hydrophilic drugs reduce Cmax, even though the clinical effect of this is recompensed by the alteration in protein-binding. The subsequent fall in plasma albumin concentration and haemodilution results in reducing the total plasma concentration of albumin-bound drugs. Moreover, placental and steroid hormones displace other drugs from their protein-binding sites. Thus, there is possibility of increasing the concentration of active (free) drug of agents, which are normally binding to albumin, and this would increase the effect of the drug. Nevertheless, the unbound drugs are distributed, metabolised and eliminated, and so the free concentration of drug is only slightly affected. In monitoring the plasma concentration of a drug, the alteration of protein binding is considered to be of clinical importance. Also, during pregnancy, body fat is enhanced by nearly 4Kg, generating a larger volume of distribution with lipophilic drugs, which has little practical importance (Dawes and Chowienczyk, 2001).

Furthermore, some CYP enzymes are induced by progesterone/oestrogen, and this results in increasing the rate of metabolism, as well as clearance, of a drug. For example, phenytoin, where other isoenzymes are competitively prevented by oestradiol and progesterone, which will lead to impairment of elimination, such as theophylline. Elimination of drugs which are secreted by the biliary system, such a rifampicin, may be attenuated because of the cholestatic property of oestrogen (Dawes and Chowienczyk, 2001). Also, during pregnancy, the activity of CYP2A6 (54%), CYP3A4 (50-100%), CYP2C9 (20%) and CYP2D6 (50%) is increased. Alteration of CYP3A4 activity results in higher metabolism of drugs such as nifedipine. The activity of both CYP2C19 and CYP1A2 reduces with later stages of pregnancy with uncertain influence on drugs (Feghali, Venkataramanan and Caritis, 2015).

Alteration of drug metabolism during the gestation period results in implication for dosing the drug. Also, during pregnancy. the increase in clearance of drugs that have a narrow therapeutic window may lead to worsening disease control and sub-therapeutic concentrations. Dosing of drugs should be adjusted in the postpartum period, when pregnancy-related changes in the activity of the metabolism enzymes resolve to avoid increasing the toxic effects (Feghali, Venkataramanan and Caritis, 2015).

Furthermore, the rate of glomerular filtration increases by 50% and the renal blood flow rises by approximately 60-80% during pregnancy, which will result in increasing the elimination of drugs which are cleared unchanged, such as digoxin and penicillin. As a result, the steady-state drug concentrations will slightly decrease and this rarely needs increasing the dose (Dawes and Chowienczyk, 2001). Renal drug elimination depends on reabsorption, GFR and tubular secretion. GFR increases by 50% in the first trimester and increases during the period of pregnancy until the last week. If the drug is only eliminated through glomerular filtration, it is expected that its renal clearance is equivalent to the alterations in GFR. In spite of the uniform rise in GFR, variations in renal tubular transport can lead to difference in the effects of drugs which are renally excreted during pregnancy (Feghali, Venkataramanan and Caritis, 2015).

1.9.3 the effect of different ethnicity on the pharmacokinetics of methadone:

Different patients have different response due to diverse and complex reasons. Researchers estimate that the genetic factors forms approximately 20-95% of the variation in drug response among different patients. Genetic effects on the metabolism of a drug interact with intrinsic (physiologic) and extrinsic (environmental, cultural, and behavioural) properties of a person to define the effect of the therapy with any pharmacologic agent. Genetic variation has effects on the outcome of a treatment and elucidates the need to monitor this topic. Also, polymorphism may impact on a drug activity through alteration in its pharmacodynamics or pharmacokinetics (Belle and Singh, 2008).

Increasingly, studies have been focused on the influence of different ethnicity or race on the pharmacokinetics and pharmacodynamics of drugs. Most of the published studies which have estimated the effect of race or ethnicity on the drug’s pharmacokinetics and pharmacodynamics involved comparisons between Asians (Chinese and Japanese) and Caucasians and minor studies with comparisons between Caucasians and Blacks (Africans or African Americans) (Johnson, 1997).

Kinetics variations among different ethnic populations are split into the classical pharmacokinetics parameters of bioavailabilty, distribution, metabolism and renal excretion. Whereas absolute bioavailability (F) is determinedby three variables, which are described mathimatically below:

F= fa fg fh


Fa: fraction of absorbed drug.

Fg: fraction of drug escaping gut metabolism.

Fh: fraction of drug escaping hepatic first-pass metabolism.

Consequently, in consideration of ethnic variation in bioavailability, bioavailability variables should be a determent.

The majority of drugs undergo passive absorption, which is not anticipated to change in different ethnic groups. For example, it was shown that there is no difference between whites and blacks in the fraction absorbed of propanol. Contrariwise, drugs which are actively absorbed will be affected by different ethnic groups. For instance, it was shown that the fraction absorbed of calcium in white girls was around 25% in comparison to premenarchal girls, which was 44%. As a result, only drugs that are absorbed through active process are influenced by racial differences (Johnson, 1997).

Furthermore, the efficacy and toxicity of a drug can vary among different ethnic and racial groups. Adjusting the dose of generic drugs as therapeutic substitution might be necessary in different ethnic groups. Toxic accumulation may occur because of drugs with slow metabolism, or the need of a substitution medical service to supply the ineffective generic drugs in some minority and racial groups (Burroughs, Maxey and Levy, 2002).


This project will focus on the impacts of elderly patients on methadone pharmacokinetics due to the reduction of the total body water and body mass. Also, alteration in kinetics of methadone in geriatrics may be because of changes in metabolism and renal excretion. This research will also discuss how pregnancy affects the pharmacokinetics of methadone as a result of the physiological changes which occur during pregnancy period. These physiological changes lead to decreased Cmax, increased Tmax and slight changes in bioavailability. Also, increase of total body water and volume of distribution (Vd) occur in pregnancy and influence the kinetics of methadone. In addition, this project will analyse the influence of methadone on the kinetics of different racial groups due to genetic changes, polymorphism of CYP enzymes, and alteration in bioavailability among different ethnic populations.


This research project will  focus on the following objectives:

  • Reviewing past published studies that involve clinical studies of methadone in geriatrics, pregnant women and different racial groups.
  • Identifying the alteration in physiochemical properties as a result of the administration of methadone to geriatrics, pregnant women and various ethnic populations.
  • Evaluating how geriatrics, pregnant women and ethnic groups influence methadone pharmacokinetics
  • Analysing whether the methadone dosage needs to be adjusted due to the changes in its kinetics in these different population groups.


Simcyp simulator software has been used by the top 10 international companies with the FDA (U.S. food and drug administration) and many other agencies. Simcyp is the most sophisticated solution to support informing product labelling and selecting the doses. It connects the in vitro data to in vivo pharmacokinetic/pharmacodynamic (PK/PD) and ADME (absorption, distribution, metabolism and elimination) outcomes to assist in exploring the possible clinical complexities prior to human studies and assist decision-making in developing drugs. This software helps in decision-making through providing information are related to clinical trial design and supports in gaining clinical trials concessions. Also, simcyp save a lot of effort, time and money by the automated prediction in vivo studies that quicken the assessment of a great number of compounds. This software optimises drug labels and was recently used in approximately 66 drug-drug interaction (DDI) studies. In addition, the simcyp simulator is easy to use and reduces risk to patients by identifying patients’ physiological and genetic characteristics (Certara, 2017).

Simcyp is a physiologically-based pharmacokinetics (PBPK) scheme. The models of PBPK are used to define first-in-human dosing, predict the drug-drug interactions (DDIs), evaluate new drug formulation and optimise clinical study design. These models also describe the action of the drug in different body tissues and each tissue is considered as physiological compartment. In every compartment, the concentration of a drug can be determined through collecting drug data, trial design information and system data. The drug data involves the drug’s physicochemical properties, metabolism and solubility information and the binding features. The trial design information includes the dose of the drug, route of administration, dosing schedule and co-administered drugs. The system data consist of demographic, physiological and biochemical data. In addition, this sofware has unique genetic, epidemiological and physiological databases that make it much easier to simulate population with different demographics and ethnicities, which enables to predict the action of a drug in various populations.

This research project will simulate trials of methadone on different population groups, including geriatrics, pregnancy and different ethnicity. These simulations will show whether the alteration in total body water, total body mass, Vd and CYP enzymes will affect the pharmacokinetics outcomes.

  1. Simcyp® Simulator validation:

First, validation of the simcyp simulator is required to ensure that the results gained are reliable, accurate and authoritative. Through the simcyp simulator software version 14, a PBPK modeling is used in incorporation with alteration in demographics and physiology of the populations to determine drug outcomes. Before simulation of methadone is conducted, validation of other models is necessary to detect its predictive power.

Figure (3): substrates that are already built-in the simcyp simulator.

Simcyp contains pre-validated compounds to use and two drugs were selected randomly to conduct validation analysis. The two selected drugs were midazolam and nifedipine to validate the model. Also, past online papers were searched to identify the clinical studies of both drugs chosen to observe their pharmacokinetics in healthy volunteers. Then, a comparison in the results between simcyp outcomes and the published studies was done for both midazolam and nifedipine. The results were almost similar between the results of simcyp and the published studies for each drug and this ensures the simcyp simulator is quite accurate.

The first study was on eight healthy volunteers, who are non-smoking (four men and four  women) with ages ranged between 21 to 23 years old and having a mean weight of 69± 6 Kg. all volunteers stopped eating food and drinking beverages which contained alcohol and caffeine from midnight before starting the investigation. These volunteers   took 7.5mg midazolam orally for five hours (300 minutes) as a single dose (Mandona et al., 1992).

The second study was on 27 healthy Caucasians (26 males), their ages varied between 19-26 years old (an average age of 25) and their weights ranged between 60-92 Kg (mean weight was 74 Kg). These healthy volunteers received 20mg of nifedipine as a single dose orally for 24 hours (Ahsan et al., 1991).

  1.          literature search:


Simcyp can be used with other drugs after it having been checked that it gave reliable results, even if the drugs are not already build-up in simcyp as a substrate. Drugs of abuse (i.e. methadone) are not in the simcyp simulator, so these drugs should be created in order to start working with them.

Previous published studies were determined after a comprehensive literature review regarding the effect of methadone on geriatrics, pregnant women and different ethnicity. Also, the clinical pharmacokinetics data were recognised to identify the drugs in simcyp to start simulations.

Databases such as PubMed, Google Scholar and ScienceDirect were used to identify suitable published clinical trials papers.

While searching for information on methadone, a search strategy were followed, in which specific terms were used to get accurate and helpful results and to detect related articles only. Different terms were used in the search, such as “geriatrics” or “elderly” or “aging” for elderly, pregnan* or “gestation” for pregnant or pregnancy, Cmax, plasma concentration and pharmacokinetics.

The criteria of inclusion and exclusion were utilised to raise the efficacy of the literature search and reduce the possibility of ambiguity. The study will include a 20-year time period of studies. Articles older than 20 years were excluded. Also, the project will include studies on geriatrics, pregnant women and different ethnical groups and find certain data, such as Cmax, Tmax, AUC, Vd, clearance, protein binding and elimination rate.

  1.        sourcing information to create methadone in simcyp:

The clinical studies, which were identified in step2, identify the suitable chemical and physical properties for methadone to create it in simcyp. The data of bioavailability (fa), molecular weight (Mw), logP, volume of distribution at steady-state (Vss), Ka, bioavailability of unbound protein (fu,p), intrinsic clearance (clint), and renal clearance (clr) were determined.

Methadone has two enantiomers – S-methadone and R-methadone. This  information is  needed for both enantiomers to build-up them in simcyp and they were found in different papers published online. A comparison between the information published in different sources is needed to ensure the most accurate results of the pharmacokinetics profiles for both R-methadone and S-methadone in healthy volunteers.

Figure(10): shows the parameters needed to build-up both S-methadone and R-methadone (Ke et al., 2014).

Absorption, distribution, metabolism and elimination information was needed, including Cmax, Tmax, Vd, AUC, Cl and plasma concentration profiles to start the simulation of both methadone enantiomers.

The study was on 10 healthy adult volunteers who were receiving 9.9mg of methadone as a single dose for five  days (96 hours) (Ke et al., 2014).

A second simulation was done for both enantiomers in simcyp with increasing the value of Ka to 2 and reducing the value of Vss from 6 to 4 and then overlaying them on the figure below.

Now, the perfect overlaying for both R-methadone and S-methadone was predicted. After ensuring the accurate results for both enantiomers and their simulation were well within the simcyp simulator software, they can now be used with various people categories, such as geriatrics, different ethnic  groups and pregnant women.

  1.         exploring scenarios: pregnant women, geriatrics and different racial groups:

Simulation will be done on S-methadone enantiomer because  its affinity to bind to targets is much higher than R-methadone. Find the article

  1. Pregnant women:

One simulation of methadone in pregnant women alone and then with phenytoin to show the impact of drug-drug interactions during the gestation period:

  • Simulation of methadone in 10 women with 10 trials. Starting from being non-pregnant to gestational week (GW) 37 (including GW1, GW10, GW20, GW30).
  • Non-pregnant women: these women will be receiving methadone for 85 days as a customised dose starting with 10mg in the first week (day1 to day7), and the dose increasing  gradually by 10mg each week until the methadone reaches 50mg at day29 in week 5. The dose will  remain 50mg from day 29 to day 85.
  • Pregnant women: here the women start their pregnancy period and they will be receiving 50mg methadone as a multiple dose, once daily for 22 days, choosing the full PBPK model with sampling time of 1000. This will be repeated for GW1, GW10, GW20, GW30 and GW37.
  • Simulation of methadone with phenytoin in 10 women with 10 trials. This will be done in non-pregnant women and in pregnant women during different gestational weeks, including GW1, GW10, GW20, GW30 andGW37.
  • Non-pregnant women: these women will be receiving methadone for 85 days as a customised dose starting with 10mg in the first week (day1 to day7), and the dose increasing  gradually by 10mg each week until the methadone reaches 50mg at day 29 in week 5. The dose will remain 50mg from day 29 to day 85 along with 300mg phenytoin as a multiple dose, once daily for 85 days.
  • Pregnant women: here the women start their pregnancy period and they will be receiving 50mg methadone as a multiple dose once daily along with 300mg phenytoin as a multiple dose once daily for 22 days, choosing the full PBPK model with sampling time of 1000. This will be repeated for GW1, GW10, GW20, GW30 and GW37.
  1. Geriatrics:

One simulation of methadone alone in multiple populations (healthy volunteers and geriatrics) and once with phenytoin to show the effect of DDI in both populations. The simulation of methadone will be in 10 subjects with 10 trials:

  • The first simulation: 50 healthy volunteers (their ages range between 20-30 years old) and 50 elderly (their ages range between 65-75 years old) will be receiving 50mg methadone as a multiple dose once daily for 22 days, choosing ADAM and the full PBPK model with sampling times of 1000.
  • The second simulation: 50 healthy volunteers (their ages range between 20-30 years old) and 50 elderly (their ages range between 65-75 years old) will be receiving 50mg methadone as a multiple dose once daily along with 300mg phenytoin as a multiple dose once daily for 22 days, choosing ADAM and the full PBPK model with sampling times of 1000.
  1. Different ethnic groups:

One simulation of methadone alone in multiple populations (healthy volunteers, Chinese and Japanese) and once with phenytoin to show the effect of DDI in those different racial populations. The simulation of methadone will be in 30 subjects with 10 trials:

  • First simulation: 100 healthy volunteers, 100 Chinese and 100 Japanese will be receiving 50mg methadone as a multiple dose once daily for 22 days, choosing  the full PBPK model with sampling times of 1000.
  • Second simulation: 100 healthy volunteers, 100 Chinese and 100 Japanese will be receiving 50mg methadone as a multiple dose once daily along with 300mg phenytoin as a multiple dose once daily for 22 days, choosing the full PBPK model with sampling times of 1000.
  1. Data analysis:

Excel spreadsheets were generated from the simcyp simulator, including the predicted pharmacokinetics of methadone, following each simulation. Information was gathered from the following data tabs:

  • Demographic data: body weight, age, serum creatinine, renal function, GFR, human serum albumin (HAS) and alpha-1 acid glycoprotein (AGP).
  • Drug population: unbound fraction of methadone.
  • Enzymatic status CYPs: enzyme abundance (CYP2B6 and CYP3A4).
  • Concentration trial profiles (Cplasma): Cmax, Tmax (time to reach maximum concentration), and unbound and systemic concentration.
  • AUC25 (SubPlasma): AUC, AUC ratio (AUC drug inhibition/AUC drug), Cmax ratio (Cmax drug inhibition/Cmax drug), Cmax and Tmax (with median values and percentiles).
  • Distribution- Vols: substrate Vss.
  • CYP2B6: shows the activity of enzymes.
  • Feto-placenta concentration (in the case of pregnancy).

The pharmacokinetics of methadone were then compared between healthy volunteers and geriatrics with and without phenytoin (inducer). Other comparison was between healthy volunteers and different racial population with and without phenytoin. Finally, there was a comparison between healthy women and pregnant women with and without phenytoin, with further comparisons made between different GW. This project has conducted some statistical analysis through one-way ANOVA. This one-way ANOVA predicts whether there are statistical changes in the different study groups (geriatric, pregnancy and different ethnicity).

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