Month: January 2016

Evaluate The Article About Therapy (Randomized Trials)

January 28, 2016 Clinical Trials, Evidence-Based Medicine No comments , , , , , , , , , , ,

Section 1 How Serious Is The Risk of Bias?

Did Intervention and Control Groups Start With The Same Prognosis?

Consider the question of whether hospital care prolongs life. A study finds that more sick people die in the hospital than in the community. We would easily reject the naive conclusion that hospital care kills people because we recognize that hospitalized patients are sicker (worse prognosis) than patients in the community. Although the logic of prognostic balance is vividly clear in comparing hospitalized patients with those in the community, it may be less obvious in other contexts.

Were Patients Randomized?

The purpose of randomization is to create groups whose prognosis, with respect to the target outcomes, is similar. The reason that studies in which patient or physician preference determines whether a patient receives treatment or control (observational studies) often yield misleading results is that morbidity and mortality result from many causes. Treatment studies attempt to determine the impact of an intervention on events such as stroke, myocardial infarction, and death – occurrences that we call the trial's target outcomes. A patient's age, the underlying severity of illness, the presence of comorbidity, and a host of other factors typically determine the frequency with which a trial's target outcome occurs (prognostic factors or determinants of outcome). If prognostic factors – either those we know about or those we do not know about – prove unbalanced between a trial's treatment and control groups, the study's outcome will be biased, either underestimating or overestimating the treatment's effect. Because known prognostic factors often influence clinicians' recommendations and patients' decisions about taking treatment, observational studies often yield biased results that may get the magnitude or even the direction of the effect wrong.

Observational studies can theoretically match patients, either in the selection of patients for study or in the subsequent statistical analysis, for known prognostic factors. However, not all prognostic factors are easily measured or characterized, and in many diseases only a few are known. Therefore, even the most careful patient selection and statistical methods are unable to completely address the bias in the estimated treatment effect. The power of randomization is that treatment and control groups are more likely to have a balanced distribution of know and unknown prognostic factors. However, although randomization is a powerful technique, it does not always succeed in creating groups with similar prognosis. Investigators may make mistakes that compromise randomization, or randomization may fail because of chance – unlikely events sometimes happen.

Was Randomization Concealed?

When those enrolling patients are unware and cannot control the arm to which the patient is allocated, we refer to randomization as concealed. In unconcealed trials, those responsible for recruitment may systematically enroll sicker – or less sick – patients to either a treatment or control group. This behavior will compromise the purpose of randomization, and the study will yield a biased result (imbalance in prognosis).

Were Patients in the Treatment and Control Groups Similar With Respect to Known Prognostic Factors? (The Importance of Sample Size)

The purpose of randomization is to create groups whose prognosis, with respect to the target outcomes, is similar. Some times, through bad luck, randomization will fail to achieve this goal. The smaller the sample size, the more likely the trial will have prognostic imbalance.

Picture a trial testing a new treatment for heart failure that is enrolling patients classified as having New York Heart Association functional class III and class IV heart failure. Patients with class IV heart failure have a much worse prognosis than those with class III heart failure. The trial is small, with only 8 patients. One would not be surprised if all 4 patients with class III heart failure were allocated to the treatment group and all 4 patients with class IV heart failure were allocated to the control group. Such a result of the allocation process would seriously bias the study in favor of the treatment. Were the trial to enroll 800 patients, one would be startled if randomization placed all 400 patients with class III heart failure in the treatment arm. The larger the sample size, the more likely randomization will achieve its goal of prognostic balance.

The smaller the sample size, the more likely the trial will have prognostic imbalance. We can check how effectively randomization has balanced known prognostic factors by looking for a display of patient characteristics of the treatment and control groups at the study's commencement – the baseline or entry prognostic features. Although we will never know whether similarity exists for the unknown prognostic factors, we are reasssured when the known prognostic factors are well balanced. All is not lost if the treatment groups are not similar at baseline. Statistical techniques permit adjustment of the study result for baseline differences. When both adjusted analyses and unadjusted analyses generate the same conclusion, clinicians gain confidence that the risk of bias is not excessive.

Was Prognostic Balance Maintained as the Study Progressed?

To What Extent Was the Study Blinded?

If randomization succeeds, treatment and control groups begin wtih a similar prognosis. Randomization, however, provides no guarantees that the 2 groups will remain prognostically balanced. Blinding is the optimal strategy for maintaining prognostic balance. There are five groups that should, if possible, be blind to treatment assignment, including:

  • Patients – to avoid placebo effects
  • Clinicians – to prevent differential administration of therapies that affect the outcome of interest (cointervention)
  • Data collectors – to prevent bias in data collection
  • Adjudicators of outcome – to prevent bias in decisions about whether or not a patient has had an outcome of interest
  • Data analysts – to avoid bias in decisions regarding data analysis

These 5 groups involved in clinical trials will remain unware of whether patients are receiving the experimental therapy or control therapy.

Were the Groups Prognostically Balanced at the Study's Completion?

It is possible for investigators to effectively conceal and blind treatment assignment and still fail to achieve an unbiased result.

Was Follow-up Complete?

Ideally, at the conclusion of a trial, investigators will know the status of each patient with respect to the target outcome. The greater the number of patients whose outcome is unknown – patients lost of follow-up – the more a study is potentially compromised. The reason is that patients who are retained – they may disappear because they have adverse outcomes or because they are doing well and so did not return for assessment. The magnitude of the bias may be substantial. See  two examples in Pharmacy Profession Forum at http://forum.tomhsiung.com/pharmacy-practice/clinical-trials/852-example-how-lost-to-follow-up-affect-the-outcome-of-a-rct.html

Loss to follow-up may substantially increase the risk of bias. If assuming a worst-case scenario does not change the inferences arising from study results, then loss to follow-up is unlikely a problem. If such an assumption would significantly alter the results, the extent to which bias is introduced depends on how likely it is that treatment patients lost to follow-up fared badly, whereas control patients lost to follow-up fared well. That decision is a matter of judgement.

Was the Trial Stopped Too Early?

Stopping trial early (i.e., before enrolling the planned sample size) when one sees an apparent large benefit is risky and may compromise randomization. These stopped early trials run the risk of greatly overestimating the treatment effect.

A trial designed with too short a follow-up also may compromise crucial information that adequate length of follow-up would reveal. For example, consider a trial that randomly assigned patients with an abdominal aortic aneurysm to either an open surgical repair or a less invasive, endovascular repair technique. At the end of the 30-day follow-up, mortality was significantly lower in the endovascular technique group. The investigators followed up participants for an additional 2 years and found that there was no difference in mortality between groups after the first year. Had the trial ended earlier, the endovascular technique may have been considered substantially better than the open surgical techinique.

Were Patients Analyzed in the Groups to Which They Were Randomized?

Investigators will undermine the benefits of randomization if they omit from the analysis patients who do not receive their assigned treatment or, worst yet, count events that occur in nonadherent patients who were assigned to treatment against the controll group. Such analyses will bias the results if the reasons for nonadherence are related to prognosis. In a number of randomized trials, patients who did not adhere to their assigned drug regimens fared worse than those who took their medication as instructed, even after taking into account all known prognostic factors. When adherent patients are destined to have a better outcome, omitting those who do not receive assigned treatment undermines the unbiased comparison provided by randomization. Investigators prevent this bias when they follow the intention-to-treat principle and analyze all patients in the group to which they were randomized irrespective of what treatment they actually received. Following the intention-to-treat principle does not, however, reduce bias associated with loss to follow-up.


Section 2 What Are the Results?

How Large Was the Treatment Effect?

Most frequently, RCTs monitor dichotomous outcomes (e.g., "yes" or "no" classifications for cancer recurrence, myocardial infarction, or death). Patients either have such an event or they do not, and the article reports the proportion of patients who develop such events. Consider, for example, a study in which 20% of a control group died but only 15% of those receiving a new treatment died. How might one express these results?

One possibility is the absolute difference (known as the absolute risk reduction [ARR] or risk difference) between the proportion who died in the control group (control group risk [CGR]) and the proportion who died in the experimental group (experimental group risk [EGR]), or CGR – EGR = 0.20 – 0.15 = 0.05. Another way to express the impact of treatment is as the RR: the risk of events among patients receiving the new treatment relative to that risk among patients in the control group, or EGR/CGR = 0.15/0.20 = 0.75.

The most commonly reported measure of dichotomous treatment effects is the complement of the RR, the RRR. It is expressed as a percentage: 1 – (EGR/CGR) x 100% = (1 – 0.75) x 100% = 25%. An RRR of 25% means that of those who would have died had they been in the control group, 25% will not die if they receive treatment; the greater the RRR, the more effective the therapy. Investigators may compute the RR during a specific period, as in a survival analysis; the relative measure of effect in such a time-to-event analysis is called the hazard ratio. When people do not specify whether they are talking about RRR or ARR – for instance, "Drug X was 30% effective in reducing the risk of death" or "The efficacy of the vaccine was 92%" – they are almost invariably taking about RRR.

How Precise Was the Estimate of the Treatment Effect?

We can never be sure of the true risk reduction; the best estimate of the true treatment effect is what we observe in a well-designed randomized trial. This estimate is called a point estimate to remind us that, although the true value lies somewhere in its neighborhood, it is unlikely to be precisely correct. Investigators often tell us the neighborhood within which the true effect likely lies by calculating CIs, a range of values within which one can be confident the true effect lies.

We usually use the 95% CI. You can consider the 95% CI as defining the range that – assuming the study has low risk of bias – includes the true RRR 95% of the time. The true RRR will generally lie beyond these extremes only 5% of the time, a property of the CI that relates closely to the conventional level of statistical significance of P <0.05.

Example

If a trial randomized 100 patients each to experimental and control groups, and there were 20 deaths in the control group and 15 deaths in the experimental group, the authors would calculate a point estimate for the RRR of 25% [(1-0.15/0.20) x 100 = 25%]. You might guess, however, that the true RRR might be much smaller or much greater than 25%, based on a difference of only 5 deaths. In fact, you might surmise that the treatment might provide no benefit (an RRR of 0%) or might even do harm (a negative RRR). And you would be right; in fact, these results are consistent with both an RRR of -38% and and RRR of nearly 59%. In other words, the 95% CI on this RRR is -38% to 59%, and the trial really has not helped us decide whether or not to offer the new treatment.

If the trial enrolled 1000 patients per group rather than 100 patients per group, and the same event rates were observed as before. There were 200 deaths in the control group and 150 deaths in the experimental group. Again, the point estimate of the RRR is 25%. In this larger trial, you might think that our confidence that the true reduction in risk is close to 25% is much greater. Actually, in the larger trial the 95% CI on the RRR for this set of results is all on the positive side of 0 and runs from 9% to 41%.

These two examples show that the larger the sample size and higher the number of outcome events in a trial, the greater our confidence that the true RRR (or any other measure of effect) is close to what we observed. As one considers values farther and farther from the point estimate, they become less and less likely to represent the truth. By the time one crosses the upper or lower bundaries of the 95% CI, the values are unlikely to represnet the true RRR. All of this assumes the study is at low risk of bias.


Section 3 How Can I Apply the Results to Patient Care?

Were the Study Patients Similar to the Patient in My Practice?

If the patient before you would have qualified for enrollment in the study, you can apply the results with considerable confidence or consider the results generalizable. Often, your patient has different attributes or characteristics from those enrolled in the trial and would not have met a study's eligibility criteria. Patients may be older or younger, may be sicker or less sick, or may have comorbid disease that would have excluded them from participation in the study.

A study result probably applies even if, for example, adult patients are 2 years too old for enrollment in the study, had more severe disease, had previously been treated with a competing therapy, or had a comorbid condition. A better approach than rigidly applying the study inclusion and exclusion criteria is to ask whether there is some compelling reason why the results do not apply to the patient. You usually will not find a compelling reason, in which case you can generalize the results to your patient with confidence.

A related issue has to do with the extent to which we can generalize findings from a study using a particular drug to another closely (or not so closely) related agent. The issue of drug class effects and how conservative one should be in assuming class effects remains controversial. Generalizing findings of surgical treatment may be even riskier. Randomized trials of carotid endarterectomy, for instance, demonstrate much lower perioperative rates of stroke and death than one might expect in one's own community, which may reflect on either the patients or surgeons (and their relative expertise) selected to participate in randomized trials.

A final issue arises when a patient fits the features of a subgroup of patients in the trial report. We encourage you to be skeptical of subgroup analyses. The treatment is likely to benefit the subgroup more or less than the other patients only if the difference in the effects of treatment in the subgroups is large and unlikely to occur by chance. Even when these conditions apply, the results may be misleading, particularly when investigators did not specify their hypotheses before the study began, if they had a large number of hypotheses, or if other studies fail to replicate the finding.

Were All Patient-Important Outcomes Considered?

Treatments are indicated when they provide important benefits. Demonstrating that  a bronchodilator produce small increments in forced expiratory volume in patients with chronic airflow limitation, that a vasodilator improves cardiac output in heart failure patients, or that a lipid-lowering agent improves lipid profiles does not provide sufficient justification for administering these drugs. In these instances, investigators have chosen substitute outcomes or surrogate outcomes rather than those that patients would consider important. What clinicians and patients require is evidence that treatments improve outcomes that are important to patients, such as reducing shortness of breath during the activities required for daily living, avoiding hospitalization for heart failure, or decreasing the risk of a major stroke.

Substitute/Surrogate Outcomes

Trial of the impact of antiarrhythmic drugs after myocardial infarction illustrate the danger of using substitute outcomes or end points. Because abnormal ventricular depolarizations were associated with a high risk of death and antiarrhythmic drugs demonstrated a reduction in abnormal ventricular depolarizations (the substitute end point), it made sense that they should reduce death. A group of investigators, performed randomized trials on 3 agents (encainide, flecainide, and moricizine) that were previously found to be effective in suppressing the substitute end point of abnormal ventricular depolarizations. The investigators had to stop the trials when they discovered that mortality was substantially higher in patients receiving antiarrhythmic treatment than in those receiving placebo. Clinicians replying on the substitue end point of arrhythmia suppression would have continued to administer the 3 drugs, to the considerable detriment of their patients.

Even when investigators report favorable effects of treatment on a patient-important outcome, you must consider whether there may be deleterious effects on other outcomes. For instance, cancer chemotherapy may lengthen life but decrease its quality. Randomized trials often fail to adequately document the toxicity or adverse effects of the experimental intervention.

Composite End Points

Composite end points represent a final dangerous trend in presenting outcomes. Like surrogate outcomes, composite end points are attractive for reducing sample size and decreasing length of follow-up. Unfortunately, they can mislead. For example, a trial that reduced a composite outcome of death, nonfatal myocardial infarction, and admission for an acute coronary syndrome actually demonstrated a trend toward increased mortality with the experimental therapy and covincing effects only on admission for an acute coronary syndrome. The composite outcome would most strongly reflect the treatment effect of the most common of the components, admission for an acute coronary syndrome, even though there is no convincing evidence the treatment reduces the risk of death or myocardial infarction.

Another long-neglected outcome is the resource implications of alternative management strategies. Health care systems face increasing resource constraints the mandate careful attention to economic analysis.

PS: Substitute/surrogate end points

In clinical trials, a surrogate endpoint (or marker) is a measure of effect of a specific treatment that may correlate with a real clinical endpoint but does not necessarily have a guaranteed relationship. The National Institutes of Health(USA) defines surrogate endpoint as "a biomarker intended to substitute for a clinical endpoint".[1][2]

Surrogate markers are used when the primary endpoint is undesired (e.g., death), or when the number of events is very small, thus making it impractical to conduct a clinical trial to gather a statistically significant number of endpoints. The FDA and other regulatory agencies will often accept evidence from clinical trials that show a direct clinical benefit to surrogate markers. [3]

A surrogate endpoint of a clinical trial is a laboratory measurement or a physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions or survives. Changes induced by a therapy on a surrogate endpoint are expected to reflect changes in a clinically meaningful endpoint. [6]

A commonly used example is cholesterol. While elevated cholesterol levels increase the likelihood for heart disease, the relationship is not linear – many people with normal cholesterol develop heart disease, and many with high cholesterol do not. "Death from heart disease" is the endpoint of interest, but "cholesterol" is the surrogate marker. A clinical trial may show that a particular drug (for example, simvastatin (Zocor)) is effective in reducing cholesterol, without showing directly that simvastatin prevents death.

Are the Likely Treatment Benefits Worth the Potential Harm and Costs?

If the results of a study apply to your patient and the outcomes are important to your patient, the next question concerns whether the probable treatment benefits are worth the associated risks, burdern, and resource requirements. A 25% reduction in the RR of death may sound impressive, but its impact on your patient may nevertheless be minimal. This notion is illustrated by using a concept called number needed to treat (NNT), the number of patients who must receive an intervention fo therapy during a specific period to prevent 1 adverse outcome or produce 1 positive outcome. See here for how to calcuate NNT: http://forum.tomhsiung.com/pharmacy-practice/pharmacy-informatics-and-drug-information/424-evidence-based-medicine-what-is-number-needed-to-treat-and-number-needed-to-harm.html

The impact of a treatment is related not only to its RRR but also to the risk of the adverse outcome it is designed to prevent. One large trial in myocardial infarction suggests that clopidogrel in addition to aspirin reduces the RR of death from a cardiovascular cause, nonfatal myocardial infarction, or stroke by approximately 20% in comparison to aspirin alone. Table 6-3 considers 2 patients presenting with acute myocardial infarction without elevation of ST segments on their electrocardiograms. Compared with aspirin alone, both patients have a RRR of approximately 20%, but the ARR is quite different between the two patients, which results in a siginifant different NNT.Screen Shot 2016-02-22 at 7.59.31 PM

A key element of the decision to start therapy, therefore, is to consider the patient's risk of the event if left untreated. For any given RRR, the higher the probability that a patient will experience an adverse outcome if we do not treat, the more likely the patient will benefit from treatment and the fewer such patients we need to treat to prevent 1 adverse outcome. Knowing the NNT assists clinicians in helping patients weigh the benefits and downsides associated with their management options. What if the siutation changes to the other end (Treatment usually will induces harm compared with control [adverse event is the nature of drugs], in this example, the harm is the increased risk of bleeding)? The answer is, for any given RRI (relative risk increasing), the higher the probability that a patient will experience an adverse outcome if we treat, the more likely the patient will get harm from treatment and the fewer such patients we need to treat to cause 1 adverse outcome.

Trading off benefits and risk also requires an accurate assessment of the adverse effects of treatment. Randomized trials with relatively small sample sizes are unsuitable for detecting rare but catastrophic adverse effects of therapy. Clinicians often must look to other sources of information – often characterized by higher risk of bias – to obtain an estimate of the adverse effects of therapy.

When determining the optimal treatment choice based on the relative benefits and harms of a therapy, the values and preferences of each individual patient must be considered. How best to communicate information to patients and how to incorporate their values into clinical decision making remain areas of active investigation in evidence-based medicine.

(The End)

Biopharmaceutics – Methods For Accessing Bioavailability

January 4, 2016 Pharmacokinetics No comments , , , , , , , , ,

Direct and indirect methods may be used to assess drug bioavailability. The in vivo bioavailability of a drug product is demonstrated by the rate and extent of drug absorption, as determined by comparsion of measured parameters, e.g., concentration of the active drug ingredient in the blood, cumulative urinary excretion rates, or pharmacological effects. For drug products that are not intended to be absorbed into the bloodstream, bioavailability may be assessed by measurements intended to reflect the rate and extent to which the active ingredient or active moiety becomes available at the site of action.

Plasma Drug Concentration

Measurement of drug concentrations in blood, plasma, or serum after drug administration is the most direct and objective way to determine systemic drug bioavailability. By appropriate blood sampling, an accurate description of the plasma drug concentration – time profile of the therapeutically active drug substance(s) can be obtained using a validated drug assay.

tmax: The time of peak plasma concentration, tmax, corresponds to the time required to reach maximum drug concentration after drug administration. At tmax, peak drug absorption occurs and the rate of drug absorption exactly equals the rate of drug elimination. Drug absorption still continues after tmax is reached, but at a slower rate. When comparing drug products, tmax can be used as an approximate indication of drug absorption rate. The value for tmax will become smaller (indicating less time required to reach peak plasma concentration) as the absorption rate for the drug becomes more rapid.

Cmax: The peak plasma drug concentration, Cmax, represents the maximum plasma drug concentration obtained after oral administration of drug. For many drugs, a relationship is found between the pharmacodynamic drug effect and the plasma drug concentration. Cmax provides indications that the drug is sufficiently systemically absorbed to provide a therapeutic response. In addition, Cmax provides warning of possibly toxic levels of drug. Although not a unit for rate, Cmax is often used in bioequivalence studies as a surrogate measure for the rate of drug bioavailability.

AUC: The area under the plasma level-time curve, AUC, is a measurement of the extent of drug bioavailability. The AUC reflects the total amount of active drug that reaches the systemic circulation. The AUC is the area under the drug plasma level-time curve from t=0 to t=∞, and is equal to the amount of unchanged drug reaching the general circulation divided by the clearance.Screen Shot 2016-01-04 at 6.16.10 PM

For many drugs, the AUC is directly proportional to dose. For example, if a single dose of a drug is increased from 250 to 1000 mg, the AUC will also show a fourfold increase. In some cases, the AUC is not directly proportional to the administered dose for all dosage levels. For example, as the dosage of drug is increased, one of the pathways for drug elimination may become saturated. Drug elimination includes the processes of metabolism and excretion. Drug metabolism is an enzyme-dependent process. For drugs such as salicylate and phenytoin, continued increase of the dose causes saturation of one of the enzyme pathways for drug metabolism and consequent prolongation of the elimination half-life. The AUC thus increases disproportionally to the increase in dose, because a smaller amount of drug is being eliminated compared with the huge administered dosage. When the AUC is not directly proportional to the dose, bioavailability of the drug is difficult to evaluate because drug kinetics may be dose dependent. Conversely, absorption may also become saturated resulting in lower-than-expected changes in AUC.

Urinary Drug Excretion Data

Urinary drug excretion data is an indirect method for estimating bioavailability. The drug must be excreted in significant quantities as unchanged drug in the urine. In addition, timely urine samples must be collected and the total amount of urinary drug excretion must be obtained.Screen Shot 2016-01-04 at 6.42.53 PM

Du: The cumulative amount of drug excreted in the urine, Du, is related directly to the total amount of drug absorbed. Experimentally, urine samples are collected periodically after administration of drug product. Each urine specimen is analyzed for free drug using a specific assay. A graph is constructed that relates the cumulative drug excreted to the collection-time interval. When the drug is almost completely eliminated, the plasma concentration approaches zero and the maximum amount of drug excreted in the urine, Du, is obtained.

dDu/dt: The rate of drug excretion. Because most drug are eliminated by a first-order rate process, the rate of drug excretion is dependent on the first-order elimination rate constant, k and the concentration of drug in plasma, Cp. In Figure 15-8, the maximum rate of drug excretion, (dDu/dt)max, is at point B, whereas the minimum rate of drug excretion is at points A and C. Thus, a graph comparing the rate of drug excretion with respect to time should be similar in shape to the plasma level-time curve for that drug (Figure 15-9).

t: The total time for the drug to be excreted. In Figures 15-8 and 15-9, the slope of the curve segment A-B is related to the rate of drug absorption, whereas point C is related to the total time required after drug administration for the drug to be absorbed and completely excreted, t=∞. The t is a useful parameter in bioequivalence studies that compare several drug products.

Acute Pharmacodynamic Effect

In some cases, the quantitative measurement of a drug in plasma or urine lacks an assay with sufficient accuracy and/or reproducibility. For locally acting, nonsystemically absorbed drug products, plasma drug concentrations may not reflect the bioavailability of the drug at the site of action. An acute pharmacodynamic effect, such as an effect on forced expiratory volume, FEV1, or skin blanching can be used as an index of drug bioavailability. In this case, the acute pharmacodynamic effect is measured over a period of time after administration of the drug product. Measurements of the pharmacodynamic effect should be made with sufficient frequency to permit a reasonable estimate for a time period at least three times the half-life of the drug. This approach may be particularly applicable to dosage forms that are not intended to deliver the active moiety to the bloodstream for systemic distribution.

The use of an acute pharmacodynamic effect to determine bioavailability generally requires demonstration of a dose-response curve. Bioavailability is determined by characterization of the dose-response curve. For bioequivalence determination, pharmacodynamic parameters including the total area under the acute pharmacodynamic effect-time curve, peak pharmacodynamic effect, and the time for peak pharmacodynamic effect are obtained from the pharmacodynamic effect-time curve. The onset time and duration of the pharmacokinetic effect may also be included in the analysis of the data. The use of pharmacodynamic endpoints for the determination of bioavailability and bioequivalence is much more variable than the measurement of plasma or urine drug concentrations.

Clinical Observations

Well-controlled clinical trials in humans may be used to determine bioavailability. However, the clinical trials approach is the least accurate, least sensitive to bioavailability differences, and most variable. The highly variable clinical responses require the use of a large patient population which increases the study costs and requires a longer time to complete compared to the other approaches for determination of bioequivalence. The FDA considers this approach only when analytical methods and pharmacodynamic methods are not available to permit use of one of the approaches described above.

In Vitro Studies

Comparative drug release/dissolution studies under certain conditions may given an indication of drug bioavailability and bioequivalence. Ideally, the in vitro drug dissolution rate should correlate with in vivo drug bioavailability (see thread http://www.tomhsiung.com/wordpress/2016/01/biopharmaceutics-to-predict-bioavailability-using-physicochemical-properties/). Comparative dissolution studies are often performed on several test formulations of the same drug during drug development. For drugs whose dissolution rate is related to the rate of systemic absorption, the test formulation that demonstrates the most rapid rate of drug dissolution in vitro will generally have the most rapid rate of drug bioavailability in vivo.

Biopharmaceutics – To Predict Bioavailability Using Physicochemical Properties

January 3, 2016 Pharmacokinetics No comments , , , , , , , ,

Key Points

Absorption Rate/Absorption Time vs. Dissolution Rate/Dissolution Time

Absorbed (percent) at time T vs. Dissolved (percent) at time t

Cmax vs. Dissolved (percent) at time t

Serum Concentration vs. Dissolved (percent) at time t


An important goal during the development of a new drug product is to find a relationship between an in vitro characteristic of the dosage form and its in vivo performance. An in vitro-in vivo correlation (IVIVC) establishes a relationship between a biological property of the drug and a physicochemical property of the drug product containing the drug substance. To establish IVIVC, some property of the drug release from the drug product in vitro, under specified conditions, must relate to in vivo drug performance. The ability to predict, accurately and precisely, the expected bioavailability characteristics of a drug from a drug product using a dissolution profile characteristics is highly desirable and would eliminate the need to perform additional clinical testing.

The biological properties most commonly used are one or more pharmacokinetic parameters obtained following the administration of the dosage form are the plasma drug concentration profile, AUC, or Cmax. The physicochemical property most commonly used to estimate drug product performance is its in vitro dissolution behavior expressed as the percent of drug released under a given set of conditions. The relationship between the two properties, biological and physicochemical, is then expressed quantitatively.

Level A Correlation

Level A correlation is the highest level of correlation and represents a point-to-point (1:1) relationship between an in vitro dissoluation and the in vivo input rate of the drug from the dosage form. Level A correlation compares the percent (%) drug released versus percent (%) drug absorbed. Generally, the percentage of drug absorbed may be calculated by the Wagner-Nelson or Loo-Riegelman procedures or by direct mathematical deconvolution, a process of mathematical resolution of blood level into an input (absorption) and an output (disposition) component. The IVIVC relationship should be deminstrated with two or more formulations with different release rates to result in corresponding differences in absorption profiles.

Level B Correlation

Level B correlation utilizes the principle of statistical moment in which the mean in vitro dissolution time is compared to etiher the mean residence time (MRT) or the mean in vivo dissolution time (MDT). MRT is the mean time that the drug molecules stay in the body. MDT is the mean time for drug dissolution.

Level C Correlation

A level C correlation is not a statistical correlation. Level C correlation establishes a single-point relationship between a dissolution parameter, such as percent dissolved in a given time and a pharmacokinetic parameter such as AUC and Cmax.

Dissolution Rate Versus Absorption Rate

If dissolution of the drug is rate limiting, a faster dissolution rate may result in a faster rate of appearance of the drug in the plasma. It may be possible to establish a correlation between the rate of dissolution and rate of absorption of the drug. The absorption rate is usually more difficult to determine than peak absorption time. Therefore, the absorption time may be used in correlating dissolution data to absorption data. In the analysis of in vitro-in vivo drug correlation, rapid drug dissolution may be distinguished from the slower drug absorption by observation of the absorption time for the preparation.

PS: The absorption time refers to the time for a constant amount of drug to be absorbed. The dissolution time refers to the time for a constant amount of drug to dissolve.

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Percent of Drug Dissolved Versus Percent of Drug Absorbed

If a drug is absorbed completely after dissolution, a linear correlation may be obtained by comparing the percentage of drug absorbed to the  percentage of drug dissolved. If the drug is absorbed slowly, which occurs when absorption is the rate-limiting step, a difference in dissolution rate of the product may not be observed. In this case, the drug would be absorbed very slowly independent of the dissolution rate.

PS: Rate-Limiting Steps in Drug Absorption

Systemic drug absorption from a drug product consists of a succession of rate processes. For solid oral, immediate-release drug products, the rate processes include 1.disintegration of the drug product and subsequent release of the drug, 2.dissolution of the drug in an aqueous environment, and 3.absorption across cell membranes into the systemic circulation. In the process of drug disintegration, dissolution, and absorption, the rate at which drug reaches the circulatory system is determined by the slowest step in the sequence. The slowest step in a series of kinetic processes is called the rate-limiting step.

Screen Shot 2016-01-03 at 2.57.38 PMExcept for controlled-release products, disintegration of a solid oral drug product is usually more rapid than drug dissolution and drug absorption. For drugs that have very poor aqueous solubility, the rate at which the drug dissolves (dissolution) is often the slowest step and therefore exerts a rate-limiting effect on drug bioavailability. In contrast, for a drug that has a high aqueous solubility, the dissolution is rapid, and the rate at which the drug crosses or permeates cell membranes is the slowest or rate-limiting step.

Maximum Plasma Concentration Versus Percent of Drug Dissolved in Vitro

When different drug formulations are tested for dissolution, a poorly formulated drug may not be completely dissolved and released, resulting in lower plasma drug concentrations. The percentage of drug released at any time interval will be greater for the more bioavailable drug product. When such drug products are tested in vivo, the peak drug serum concentration will be higher for the drug product that shows the highest percent of drug dissolved.

PS: Failure of Correlation of In Vitro Dissolution to In Vivo Absorption. When a proper dissolution method is chosen, the rate of dissolution of the product may be correlated to the rate of absorption of the drug into the body. Well-defined in vitro-in vivo correlations have been reported for modified-released drug products but have been more difficult to predict for immediate-release drug products. Immediate release and rapidly dissolving drug products often have rapid absorption. Only a few plasma drug concentration values can be obtained prior to tmax and only one or two dissolution samples may be obtained.

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Serum Drug Concentration Versus Percent of Drug Dissolved

In a stduy on aspirin absorption, the serum concentration of aspirin was correlated to the percent of drug dissolved using an in vitro dissolution method. The dissolution medium was simulated gastric juice. Because aspirin is rapidly absorbed from the stomach, the dissolution of the drug is the rate-limiting step, and various formulations with different dissolution rates will cause differences in the serum concentration of aspirin by minutes.

Screen Shot 2016-01-03 at 2.59.10 PMThe Biopharmaceutic Drug Classification System

The Biopharmaceutics Drug Classification System, BCS, is a predictive approach to relate certain physiocochemical characteristics of a drug substance and drug product to in vivo bioavailability. The drugs are classified in BCS on the basis of solubility, permeability, and dissolution. The BCS is not a direct in vitro-in vivo correlation. The BCS classes are as follows: Class I – high permeability, high solubility; Class II – high permeability, low solubility; Class III – low permeability, high solubility; and Class IV – low permeability, low solubility.