Drug design

From Wikipedia, the free encyclopedia

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target.^[1] The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques.^[2] This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design.^[2] In addition to small molecules, biopharmaceuticals and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.^[3]

The phrase "drug design" is to some extent a misnomer. A more accurate term is ligand design (i.e., design of a molecule that will bind tightly to its target).^[4] Although design techniques for prediction of binding affinity are reasonably successful, there are many other properties, such as bioavailability, metabolic half-life, side effects, etc., that first must be optimized before a ligand can become a safe and efficacious drug. These other characteristics are often difficult to predict with rational design techniques. Nevertheless, due to high attrition rates, especially during clinical phases of drug development, more attention is being focused early in the drug design process on selecting candidate drugs whose physicochemical properties are predicted to result in fewer complications during development and hence more likely to lead to an approved, marketed drug.^[5] Furthermore, in vitro experiments complemented with computation methods are increasingly used in early drug discovery to select compounds with more favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles.^[6]

Drug targets

A biomolecular target (most commonly a protein or nucleic acid) is a key molecule involved in a particular metabolic or signaling pathway that is associated with a specific disease condition or pathology or to the infectivity or survival of a microbial pathogen. Potential drug targets are not necessarily disease causing but must by definition be disease modifying.^[7] In some cases, small molecules will be designed to enhance or inhibit the target function in the specific disease modifying pathway. Small molecules (for example receptor agonists, antagonists, inverse agonists, or modulators; enzyme activators or inhibitors; or ion channel openers or blockers)^[8] will be designed that are complementary to the binding site of target.^[9] Small molecules (drugs) can be designed so as not to affect any other important "off-target" molecules (often referred to as antitargets) since drug interactions with off-target molecules may lead to undesirable side effects.^[10] Due to similarities in binding sites, closely related targets identified through sequence homology have the highest chance of cross reactivity and hence highest side effect potential.

Most commonly, drugs are organic small molecules produced through chemical synthesis, but biopolymer-based drugs (also known as biopharmaceuticals) produced through biological processes are becoming increasingly more common.^[11] In addition, mRNA-based gene silencing technologies may have therapeutic applications.^[12]

Rational drug discovery

In contrast to traditional methods of drug discovery (known as forward pharmacology), which rely on trial-and-error testing of chemical substances on cultured cells or animals, and matching the apparent effects to treatments, rational drug design (also called reverse pharmacology) begins with a hypothesis that modulation of a specific biological target may have therapeutic value. In order for a biomolecule to be selected as a drug target, two essential pieces of information are required. The first is evidence that modulation of the target will be disease modifying. This knowledge may come from, for example, disease linkage studies that show an association between mutations in the biological target and certain disease states.^[13] The second is that the target is "druggable". This means that it is capable of binding to a small molecule and that its activity can be modulated by the small molecule.^[14]

Once a suitable target has been identified, the target is normally cloned and produced and purified. The purified protein is then used to establish a screening assay. In addition, the three-dimensional structure of the target may be determined.

The search for small molecules that bind to the target is begun by screening libraries of potential drug compounds. This may be done by using the screening assay (a "wet screen"). In addition, if the structure of the target is available, a virtual screen may be performed of candidate drugs. Ideally the candidate drug compounds should be "drug-like", that is they should possess properties that are predicted to lead to oral bioavailability, adequate chemical and metabolic stability, and minimal toxic effects.^[15] Several methods are available to estimate druglikeness such as Lipinski's Rule of Five and a range of scoring methods such as lipophilic efficiency.^[16] Several methods for predicting drug metabolism have also been proposed in the scientific literature.^[17]

Due to the large number of drug properties that must be simultaneously optimized during the design process, multi-objective optimization techniques are sometimes employed.^[18] Finally because of the limitations in the current methods for prediction of activity, drug design is still very much reliant on serendipity^[19] and bounded rationality.^[20]

Computer-aided drug design

The most fundamental goal in drug design is to predict whether a given molecule will bind to a target and if so how strongly. Molecular mechanics or molecular dynamics is most often used to estimate the strength of the intermolecular interaction between the small molecule and its biological target. These methods are also used to predict the conformation of the small molecule and to model conformational changes in the target that may occur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistry methods, or density functional theory are often used to provide optimized parameters for the molecular mechanics calculations and also provide an estimate of the electronic properties (electrostatic potential, polarizability, etc.) of the drug candidate that will influence binding affinity.^[21]

Molecular mechanics methods may also be used to provide semi-quantitative prediction of the binding affinity. Also, knowledge-based scoring function may be used to provide binding affinity estimates. These methods use linear regression, machine learning, neural nets or other statistical techniques to derive predictive binding affinity equations by fitting experimental affinities to computationally derived interaction energies between the small molecule and the target.^[22][23]

Ideally, the computational method will be able to predict affinity before a compound is synthesized and hence in theory only one compound needs to be synthesized, saving enormous time and cost. The reality is that present computational methods are imperfect and provide, at best, only qualitatively accurate estimates of affinity. In practice it still takes several iterations of design, synthesis, and testing before an optimal drug is discovered. Computational methods have accelerated discovery by reducing the number of iterations required and have often provided novel structures.^[24][25]

Drug design with the help of computers may be used at any of the following stages of drug discovery:

1. hit identification using virtual screening (structure- or ligand-based design)
2. hit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.)
3. lead optimization of other pharmaceutical properties while maintaining affinity

Flowchart of a Usual Clustering Analysis for Structure-Based Drug Design

In order to overcome the insufficient prediction of binding affinity calculated by recent scoring functions, the protein-ligand interaction and compound 3D structure information are used for analysis. For structure-based drug design, several post-screening analyses focusing on protein-ligand interaction have been developed for improving enrichment and effectively mining potential candidates:

• Consensus scoring^[26][27]
  • Selecting candidates by voting of multiple scoring functions
  • May lose the relationship between protein-ligand structural information and scoring criterion
• Cluster analysis^[28][29]
  • Represent and cluster candidates according to protein-ligand 3D information
  • Needs meaningful representation of protein-ligand interactions.

Types

Drug discovery cycle highlighting both ligand-based (indirect) and structure-based (direct) drug design strategies.

There are two major types of drug design. The first is referred to as ligand-based drug design and the second, structure-based drug design.^[2]

Ligand-based

Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the biological target of interest. These other molecules may be used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.^[30] In other words, a model of the biological target may be built based on the knowledge of what binds to it, and this model in turn may be used to design new molecular entities that interact with the target. Alternatively, a quantitative structure-activity relationship (QSAR), in which a correlation between calculated properties of molecules and their experimentally determined biological activity, may be derived. These QSAR relationships in turn may be used to predict the activity of new analogs.^[31]

Structure-based

Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target obtained through methods such as x-ray crystallography or NMR spectroscopy.^[32] If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein. Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist. Alternatively various automated computational procedures may be used to suggest new drug candidates.^[33]

Current methods for structure-based drug design can be divided roughly into three main categories.^[34] The first method is identification of new ligands for a given receptor by searching large databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs. This method is known as virtual screening. A second category is de novo design of new ligands. In this method, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested.^[35][36][37] A third method is the optimization of known ligands by evaluating proposed analogs within the binding cavity.^[34]

Binding site identification

Binding site identification is the first step in structure based design.^[14][38] If the structure of the target or a sufficiently similar homolog is determined in the presence of a bound ligand, then the ligand should be observable in the structure in which case location of the binding site is trivial. However, there may be unoccupied allosteric binding sites that may be of interest. Furthermore, it may be that only apoprotein (protein without ligand) structures are available and the reliable identification of unoccupied sites that have the potential to bind ligands with high affinity is non-trivial. In brief, binding site identification usually relies on identification of concave surfaces on the protein that can accommodate drug sized molecules that also possess appropriate "hot spots" (hydrophobic surfaces, hydrogen bonding sites, etc.) that drive ligand binding.^[14][38]

Scoring functions

Structure-based drug design attempts to use the structure of proteins as a basis for designing new ligands by applying the principles of molecular recognition. Selective high affinity binding to the target is generally desirable since it leads to more efficacious drugs with fewer side effects. Thus, one of the most important principles for designing or obtaining potential new ligands is to predict the binding affinity of a certain ligand to its target (and known antitargets) and use the predicted affinity as a criterion for selection.^[39]
One early general-purposed empirical scoring function to describe the binding energy of ligands to receptors was developed by Böhm.^[40][41] This empirical scoring function took the form:

${\displaystyle \Delta G_{\text{bind}}=\Delta G_{\text{0}}+\Delta G_{\text{hb}}\Sigma _{h-bonds}+\Delta G_{\text{ionic}}\Sigma _{ionic-int}+\Delta G_{\text{lipophilic}}\left\vert A\right\vert +\Delta G_{\text{rot}}{\mathit {NROT}}}$

where:

• ΔG₀ – empirically derived offset that in part corresponds to the overall loss of translational and rotational entropy of the ligand upon binding.
• ΔG_hb – contribution from hydrogen bonding
• ΔG_ionic – contribution from ionic interactions
• ΔG_lip – contribution from lipophilic interactions where |A_lipo| is surface area of lipophilic contact between the ligand and receptor
• ΔG_rot – entropy penalty due to freezing a rotatable in the ligand bond upon binding

A more general thermodynamic "master" equation is as follows:^[42]

${\displaystyle {\begin{array}{lll}\Delta G_{\text{bind}}=-RT\ln K_{\text{d}}\\[1.3ex]K_{\text{d}}={\dfrac {[{\text{Ligand}}][{\text{Receptor}}]}{[{\text{Complex}}]}}\\[1.3ex]\Delta G_{\text{bind}}=\Delta G_{\text{desolvation}}+\Delta G_{\text{motion}}+\Delta G_{\text{configuration}}+\Delta G_{\text{interaction}}\end{array}}}$

where:

• desolvation – enthalpic penalty for removing the ligand from solvent
• motion – entropic penalty for reducing the degrees of freedom when a ligand binds to its receptor
• configuration – conformational strain energy required to put the ligand in its "active" conformation
• interaction – enthalpic gain for "resolvating" the ligand with its receptor

The basic idea is that the overall binding free energy can be decomposed into independent components that are known to be important for the binding process. Each component reflects a certain kind of free energy alteration during the binding process between a ligand and its target receptor. The Master Equation is the linear combination of these components. According to Gibbs free energy equation, the relation between dissociation equilibrium constant, K_d, and the components of free energy was built.

Various computational methods are used to estimate each of the components of the master equation. For example, the change in polar surface area upon ligand binding can be used to estimate the desolvation energy. The number of rotatable bonds frozen upon ligand binding is proportional to the motion term. The configurational or strain energy can be estimated using molecular mechanics calculations. Finally the interaction energy can be estimated using methods such as the change in non polar surface, statistically derived potentials of mean force, the number of hydrogen bonds formed, etc. In practice, the components of the master equation are fit to experimental data using multiple linear regression. This can be done with a diverse training set including many types of ligands and receptors to produce a less accurate but more general "global" model or a more restricted set of ligands and receptors to produce a more accurate but less general "local" model.^[43]

Examples

A particular example of rational drug design involves the use of three-dimensional information about biomolecules obtained from such techniques as X-ray crystallography and NMR spectroscopy. Computer-aided drug design in particular becomes much more tractable when there is a high-resolution structure of a target protein bound to a potent ligand. This approach to drug discovery is sometimes referred to as structure-based drug design. The first unequivocal example of the application of structure-based drug design leading to an approved drug is the carbonic anhydrase inhibitor dorzolamide, which was approved in 1995.^[44][45]

Another important case study in rational drug design is imatinib, a tyrosine kinase inhibitor designed specifically for the bcr-abl fusion protein that is characteristic for Philadelphia chromosome-positive leukemias (chronic myelogenous leukemia and occasionally acute lymphocytic leukemia). Imatinib is substantially different from previous drugs for cancer, as most agents of chemotherapy simply target rapidly dividing cells, not differentiating between cancer cells and other tissues.^[46]

Additional examples include:

• Many of the atypical antipsychotics
• Cimetidine, the prototypical H₂-receptor antagonist from which the later members of the class were developed
• Selective COX-2 inhibitor NSAIDs
• Enfuvirtide, a peptide HIV entry inhibitor
• Nonbenzodiazepines like zolpidem and zopiclone
• Raltegravir, an HIV integrase inhibitor^[47]
• SSRIs (selective serotonin reuptake inhibitors), a class of antidepressants
• Zanamivir, an antiviral drug

Case Studies

• 5-HT3 antagonists
• Acetylcholine receptor agonists
• Angiotensin receptor antagonists
• Bcr-Abl tyrosine-kinase inhibitors
• Cannabinoid receptor antagonists
• CCR5 receptor antagonists
• Cyclooxygenase 2 inhibitors
• Dipeptidyl peptidase-4 inhibitors
• HIV protease inhibitors
• NK1 receptor antagonists
• Non-nucleoside reverse transcriptase inhibitors
• Nucleoside and nucleotide reverse transcriptase inhibitors
• PDE5 inhibitors
• Proton pump inibitors
• Renin inhibitors
• Triptans
• TRPV1 antagonists
• c-Met inhibitors

Criticism

It has been argued that the highly rigid and focused nature of rational drug design suppresses serendipity in drug discovery.^[48] Because many of the most significant medical discoveries have been inadvertent, the recent focus on rational drug design may limit the progress of drug discovery. Furthermore, the rational design of a drug may be limited by a crude or incomplete understanding of the underlying molecular processes of the disease it is intended to treat.

Randomized controlled trial

From Wikipedia, the free encyclopedia

Flowchart of four phases (enrollment, intervention allocation, follow-up, and data analysis) of a parallel randomized trial of two groups, modified from the CONSORT (Consolidated Standards of Reporting Trials) 2010 Statement^[1]

A randomized controlled trial (or randomized control trial;^[2] RCT) is a type of scientific (often medical) experiment which aims to reduce bias when testing a new treatment. The people participating in the trial are randomly allocated to either the group receiving the treatment under investigation or to a group receiving standard treatment (or placebo treatment) as the control. Randomization minimises selection bias and the different comparison groups allow the researchers to determine any effects of the treatment when compared with the no treatment (control) group, while other variables are kept constant. The RCT is often considered the gold standard for a clinical trial. RCTs are often used to test the efficacy or effectiveness of various types of medical intervention and may provide information about adverse effects, such as drug reactions. Random assignment of intervention is done after subjects have been assessed for eligibility and recruited, but before the intervention to be studied begins.

Random allocation in real trials is complex, but conceptually the process is like tossing a coin. After randomization, the two (or more) groups of subjects are followed in exactly the same way and the only differences between them is the care they receive. For example, in terms of procedures, tests, outpatient visits, and follow-up calls, should be those intrinsic to the treatments being compared. The most important advantage of proper randomization is that it minimizes allocation bias, balancing both known and unknown prognostic factors, in the assignment of treatments.^[3]

The terms "RCT" and randomized trial are sometimes used synonymously, but the methodologically sound practice is to reserve the "RCT" name only for trials that contain control groups, in which groups receiving the experimental treatment are compared with control groups receiving no treatment (a placebo-controlled study) or a previously tested treatment (a positive-control study). The term "randomized trials" omits mention of controls and can describe studies that compare multiple treatment groups with each other (in the absence of a control group).^[4] Similarly, although the "RCT" name is sometimes expanded as "randomized clinical trial" or "randomized comparative trial", the methodologically sound practice, to avoid ambiguity in the scientific literature, is to retain "control" in the definition of "RCT" and thus reserve that name only for trials that contain controls. Not all randomized clinical trials are randomized controlled trials (and some of them could never be, in cases where controls would be impractical or unethical to institute). The term randomized controlled clinical trials is a methodologically sound alternate expansion for "RCT" in RCTs that concern clinical research;^[5][6][7] however, RCTs are also employed in other research areas, including many of the social sciences.

History

The first reported clinical trial was conducted by James Lind in 1747 to identify treatment for scurvy.^[8] Randomized experiments appeared in psychology, where they were introduced by Charles Sanders Peirce,^[9] and in education.^[10][11][12] Later, randomized experiments appeared in agriculture, due to Jerzy Neyman^[13] and Ronald A. Fisher. Fisher's experimental research and his writings popularized randomized experiments.^[14]

The first published RCT in medicine appeared in the 1948 paper entitled "Streptomycin treatment of pulmonary tuberculosis", which described a Medical Research Council investigation.^[15][16][17] One of the authors of that paper was Austin Bradford Hill, who is credited as having conceived the modern RCT.^[18]

By the late 20th century, RCTs were recognized as the standard method for "rational therapeutics" in medicine.^[19] As of 2004, more than 150,000 RCTs were in the Cochrane Library.^[18] To improve the reporting of RCTs in the medical literature, an international group of scientists and editors published Consolidated Standards of Reporting Trials (CONSORT) Statements in 1996, 2001 and 2010, and these have become widely accepted.^[1][3] Randomization is the process of assigning trial subjects to treatment or control groups using an element of chance to determine the assignments in order to reduce the bias.

Ethics

Although the principle of clinical equipoise ("genuine uncertainty within the expert medical community... about the preferred treatment") common to clinical trials^[20] has been applied to RCTs, the ethics of RCTs have special considerations. For one, it has been argued that equipoise itself is insufficient to justify RCTs.^[21] For another, "collective equipoise" can conflict with a lack of personal equipoise (e.g., a personal belief that an intervention is effective).^[22] Finally, Zelen's design, which has been used for some RCTs, randomizes subjects before they provide informed consent, which may be ethical for RCTs of screening and selected therapies, but is likely unethical "for most therapeutic trials."^[23][24]

Although subjects almost always provide informed consent for their participation in an RCT, studies since 1982 have documented that RCT subjects may believe that they are certain to receive treatment that is best for them personally; that is, they do not understand the difference between research and treatment.^[25][26] Further research is necessary to determine the prevalence of and ways to address this "therapeutic misconception".^[26]

The RCT method variations may also create cultural effects that have not been well understood.^[27] For example, patients with terminal illness may join trials in the hope of being cured, even when treatments are unlikely to be successful.

Trial registration

In 2004, the International Committee of Medical Journal Editors (ICMJE) announced that all trials starting enrolment after July 1, 2005 must be registered prior to consideration for publication in one of the 12 member journals of the committee.^[28] However, trial registration may still occur late or not at all.^[29][30] Medical journals have been slow in adapting policies requiring mandatory clinical trial registration as a prerequisite for publication.^[31]

Classifications

By study design

One way to classify RCTs is by study design. From most to least common in the healthcare literature, the major categories of RCT study designs are:^[32]

• Parallel-group – each participant is randomly assigned to a group, and all the participants in the group receive (or do not receive) an intervention.
• Crossover – over time, each participant receives (or does not receive) an intervention in a random sequence.^[33][34]
• Cluster – pre-existing groups of participants (e.g., villages, schools) are randomly selected to receive (or not receive) an intervention.
• Factorial – each participant is randomly assigned to a group that receives a particular combination of interventions or non-interventions (e.g., group 1 receives vitamin X and vitamin Y, group 2 receives vitamin X and placebo Y, group 3 receives placebo X and vitamin Y, and group 4 receives placebo X and placebo Y).

An analysis of the 616 RCTs indexed in PubMed during December 2006 found that 78% were parallel-group trials, 16% were crossover, 2% were split-body, 2% were cluster, and 2% were factorial.^[32]

By outcome of interest (efficacy vs. effectiveness)

RCTs can be classified as "explanatory" or "pragmatic."^[35] Explanatory RCTs test efficacy in a research setting with highly selected participants and under highly controlled conditions.^[35] In contrast, pragmatic RCTs (pRCTs) test effectiveness in everyday practice with relatively unselected participants and under flexible conditions; in this way, pragmatic RCTs can "inform decisions about practice."^[35]

By hypothesis (superiority vs. noninferiority vs. equivalence)

Another classification of RCTs categorizes them as "superiority trials", "noninferiority trials", and "equivalence trials", which differ in methodology and reporting.^[36] Most RCTs are superiority trials, in which one intervention is hypothesized to be superior to another in a statistically significant way.^[36] Some RCTs are noninferiority trials "to determine whether a new treatment is no worse than a reference treatment."^[36] Other RCTs are equivalence trials in which the hypothesis is that two interventions are indistinguishable from each other.^[36]

Randomization

The advantages of proper randomization in RCTs include:^[37]

• "It eliminates bias in treatment assignment," specifically selection bias and confounding.
• "It facilitates blinding (masking) of the identity of treatments from investigators, participants, and assessors."
• "It permits the use of probability theory to express the likelihood that any difference in outcome between treatment groups merely indicates chance."

There are two processes involved in randomizing patients to different interventions. First is choosing a randomization procedure to generate an unpredictable sequence of allocations; this may be a simple random assignment of patients to any of the groups at equal probabilities, may be "restricted", or may be "adaptive." A second and more practical issue is allocation concealment, which refers to the stringent precautions taken to ensure that the group assignment of patients are not revealed prior to definitively allocating them to their respective groups. Non-random "systematic" methods of group assignment, such as alternating subjects between one group and the other, can cause "limitless contamination possibilities" and can cause a breach of allocation concealment.^[38]

However empirical evidence that adequate randomization changes outcomes relative to inadequate randomization has been difficult to detect.^[39]

Procedures

The treatment allocation is the desired proportion of patients in each treatment arm.

An ideal randomization procedure would achieve the following goals:^[40]

• Maximize statistical power, especially in subgroup analyses. Generally, equal group sizes maximize statistical power, however, unequal groups sizes maybe more powerful for some analyses (e.g., multiple comparisons of placebo versus several doses using Dunnett’s procedure^[41] ), and are sometimes desired for non-analytic reasons (e.g., patients maybe more motivated to enroll if there is a higher chance of getting the test treatment, or regulatory agencies may require a minimum number of patients exposed to treatment).^[42]
• Minimize selection bias. This may occur if investigators can consciously or unconsciously preferentially enroll patients between treatment arms. A good randomization procedure will be unpredictable so that investigators cannot guess the next subject's group assignment based on prior treatment assignments. The risk of selection bias is highest when previous treatment assignments are known (as in unblinded studies) or can be guessed (perhaps if a drug has distinctive side effects).
• Minimize allocation bias (or confounding). This may occur when covariates that affect the outcome are not equally distributed between treatment groups, and the treatment effect is confounded with the effect of the covariates (i.e., an "accidental bias"^[37][43]). If the randomization procedure causes an imbalance in covariates related to the outcome across groups, estimates of effect may be biased if not adjusted for the covariates (which may be unmeasured and therefore impossible to adjust for).

However, no single randomization procedure meets those goals in every circumstance, so researchers must select a procedure for a given study based on its advantages and disadvantages.

Simple

This is a commonly used and intuitive procedure, similar to "repeated fair coin-tossing."^[37] Also known as "complete" or "unrestricted" randomization, it is robust against both selection and accidental biases. However, its main drawback is the possibility of imbalanced group sizes in small RCTs. It is therefore recommended only for RCTs with over 200 subjects.^[44]

Restricted

To balance group sizes in smaller RCTs, some form of "restricted" randomization is recommended.^[44] The major types of restricted randomization used in RCTs are:

• Permuted-block randomization or blocked randomization: a "block size" and "allocation ratio" (number of subjects in one group versus the other group) are specified, and subjects are allocated randomly within each block.^[38] For example, a block size of 6 and an allocation ratio of 2:1 would lead to random assignment of 4 subjects to one group and 2 to the other. This type of randomization can be combined with "stratified randomization", for example by center in a multicenter trial, to "ensure good balance of participant characteristics in each group."^[3] A special case of permuted-block randomization is random allocation, in which the entire sample is treated as one block.^[38] The major disadvantage of permuted-block randomization is that even if the block sizes are large and randomly varied, the procedure can lead to selection bias.^[40] Another disadvantage is that "proper" analysis of data from permuted-block-randomized RCTs requires stratification by blocks.^[44]
• Adaptive biased-coin randomization methods (of which urn randomization is the most widely known type): In these relatively uncommon methods, the probability of being assigned to a group decreases if the group is overrepresented and increases if the group is underrepresented.^[38] The methods are thought to be less affected by selection bias than permuted-block randomization.^[44]

Adaptive

At least two types of "adaptive" randomization procedures have been used in RCTs, but much less frequently than simple or restricted randomization:

• Covariate-adaptive randomization, of which one type is minimization: The probability of being assigned to a group varies in order to minimize "covariate imbalance."^[44] Minimization is reported to have "supporters and detractors"^[38] because only the first subject's group assignment is truly chosen at random, the method does not necessarily eliminate bias on unknown factors.^[3]
• Response-adaptive randomization, also known as outcome-adaptive randomization: The probability of being assigned to a group increases if the responses of the prior patients in the group were favorable.^[44] Although arguments have been made that this approach is more ethical than other types of randomization when the probability that a treatment is effective or ineffective increases during the course of an RCT, ethicists have not yet studied the approach in detail.^[45]

Allocation concealment

"Allocation concealment" (defined as "the procedure for protecting the randomization process so that the treatment to be allocated is not known before the patient is entered into the study") is important in RCTs.^[46] In practice, clinical investigators in RCTs often find it difficult to maintain impartiality. Stories abound of investigators holding up sealed envelopes to lights or ransacking offices to determine group assignments in order to dictate the assignment of their next patient.^[38] Such practices introduce selection bias and confounders (both of which should be minimized by randomization), thereby possibly distorting the results of the study.^[38] Adequate allocation concealment should defeat patients and investigators from discovering treatment allocation once a study is underway and after the study has concluded. Treatment related side-effects or adverse events may be specific enough to reveal allocation to investigators or patients thereby introducing bias or influencing any subjective parameters collected by investigators or requested from subjects.

Some standard methods of ensuring allocation concealment include sequentially numbered, opaque, sealed envelopes (SNOSE); sequentially numbered containers; pharmacy controlled randomization; and central randomization.^[38] It is recommended that allocation concealment methods be included in an RCT's protocol, and that the allocation concealment methods should be reported in detail in a publication of an RCT's results; however, a 2005 study determined that most RCTs have unclear allocation concealment in their protocols, in their publications, or both.^[47] On the other hand, a 2008 study of 146 meta-analyses concluded that the results of RCTs with inadequate or unclear allocation concealment tended to be biased toward beneficial effects only if the RCTs' outcomes were subjective as opposed to objective.^[48]

Sample size

The number of treatment units (subjects or groups of subjects) assigned to control and treatment groups, affects an RCT's reliability. If the effect of the treatment is small, the number of treatment units in either group may be insufficient for rejecting the null hypothesis in the respective statistical test. The failure to reject the null hypothesis would imply that the treatment shows no statistically significant effect on the treated in a given test. But as the sample size increases, the same RCT may be able to demonstrate a significant effect of the treatment, even if this effect is small.^[49]

Blinding

An RCT may be blinded, (also called "masked") by "procedures that prevent study participants, caregivers, or outcome assessors from knowing which intervention was received."^[48] Unlike allocation concealment, blinding is sometimes inappropriate or impossible to perform in an RCT; for example, if an RCT involves a treatment in which active participation of the patient is necessary (e.g., physical therapy), participants cannot be blinded to the intervention.

Traditionally, blinded RCTs have been classified as "single-blind", "double-blind", or "triple-blind"; however, in 2001 and 2006 two studies showed that these terms have different meanings for different people.^[50][51] The 2010 CONSORT Statement specifies that authors and editors should not use the terms "single-blind", "double-blind", and "triple-blind"; instead, reports of blinded RCT should discuss "If done, who was blinded after assignment to interventions (for example, participants, care providers, those assessing outcomes) and how."^[3]

RCTs without blinding are referred to as "unblinded",^[52] "open",^[53] or (if the intervention is a medication) "open-label".^[54] In 2008 a study concluded that the results of unblinded RCTs tended to be biased toward beneficial effects only if the RCTs' outcomes were subjective as opposed to objective;^[48] for example, in an RCT of treatments for multiple sclerosis, unblinded neurologists (but not the blinded neurologists) felt that the treatments were beneficial.^[55] In pragmatic RCTs, although the participants and providers are often unblinded, it is "still desirable and often possible to blind the assessor or obtain an objective source of data for evaluation of outcomes."^[35]

Analysis of data

The types of statistical methods used in RCTs depend on the characteristics of the data and include:

• For dichotomous (binary) outcome data, logistic regression (e.g., to predict sustained virological response after receipt of peginterferon alfa-2a for hepatitis C^[56]) and other methods can be used.
• For continuous outcome data, analysis of covariance (e.g., for changes in blood lipid levels after receipt of atorvastatin after acute coronary syndrome^[57]) tests the effects of predictor variables.
• For time-to-event outcome data that may be censored, survival analysis (e.g., Kaplan–Meier estimators and Cox proportional hazards models for time to coronary heart disease after receipt of hormone replacement therapy in menopause^[58]) is appropriate.

Regardless of the statistical methods used, important considerations in the analysis of RCT data include:

• Whether an RCT should be stopped early due to interim results. For example, RCTs may be stopped early if an intervention produces "larger than expected benefit or harm", or if "investigators find evidence of no important difference between experimental and control interventions."^[3]
• The extent to which the groups can be analyzed exactly as they existed upon randomization (i.e., whether a so-called "intention-to-treat analysis" is used). A "pure" intention-to-treat analysis is "possible only when complete outcome data are available" for all randomized subjects;^[59] when some outcome data are missing, options include analyzing only cases with known outcomes and using imputed data.^[3] Nevertheless, the more that analyses can include all participants in the groups to which they were randomized, the less bias that an RCT will be subject to.^[3]
• Whether subgroup analysis should be performed. These are "often discouraged" because multiple comparisons may produce false positive findings that cannot be confirmed by other studies.^[3]

Reporting of results

The CONSORT 2010 Statement is "an evidence-based, minimum set of recommendations for reporting RCTs."^[60] The CONSORT 2010 checklist contains 25 items (many with sub-items) focusing on "individually randomised, two group, parallel trials" which are the most common type of RCT.^[1]

For other RCT study designs, "CONSORT extensions" have been published, some examples are:

• Consort 2010 Statement: Extension to Cluster Randomised Trials^[61]
• Consort 2010 Statement: Non-Pharmacologic Treatment Interventions^[62][63]

Relative importance and observational studies

Two studies published in The New England Journal of Medicine in 2000 found that observational studies and RCTs overall produced similar results.^[64][65] The authors of the 2000 findings questioned the belief that "observational studies should not be used for defining evidence-based medical care" and that RCTs' results are "evidence of the highest grade."^[64][65] However, a 2001 study published in Journal of the American Medical Association concluded that "discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common" between observational studies and RCTs.^[66]

Two other lines of reasoning question RCTs' contribution to scientific knowledge beyond other types of studies:

• If study designs are ranked by their potential for new discoveries, then anecdotal evidence would be at the top of the list, followed by observational studies, followed by RCTs.^[67]
• RCTs may be unnecessary for treatments that have dramatic and rapid effects relative to the expected stable or progressively worse natural course of the condition treated.^[68][69] One example is combination chemotherapy including cisplatin for metastatic testicular cancer, which increased the cure rate from 5% to 60% in a 1977 non-randomized study.^[69][70]

Interpretation of statistical results

Like all statistical methods, RCTs are subject to both type I ("false positive") and type II ("false negative") statistical errors. Regarding Type I errors, a typical RCT will use 0.05 (i.e., 1 in 20) as the probability that the RCT will falsely find two equally effective treatments significantly different.^[71] Regarding Type II errors, despite the publication of a 1978 paper noting that the sample sizes of many "negative" RCTs were too small to make definitive conclusions about the negative results,^[72] by 2005-2006 a sizeable proportion of RCTs still had inaccurate or incompletely reported sample size calculations.^[73]

Advantages

RCTs are considered to be the most reliable form of scientific evidence in the hierarchy of evidence that influences healthcare policy and practice because RCTs reduce spurious causality and bias. Results of RCTs may be combined in systematic reviews which are increasingly being used in the conduct of evidence-based practice. Some examples of scientific organizations' considering RCTs or systematic reviews of RCTs to be the highest-quality evidence available are:

• As of 1998, the National Health and Medical Research Council of Australia designated "Level I" evidence as that "obtained from a systematic review of all relevant randomised controlled trials" and "Level II" evidence as that "obtained from at least one properly designed randomised controlled trial."^[74]
• Since at least 2001, in making clinical practice guideline recommendations the United States Preventive Services Task Force has considered both a study's design and its internal validity as indicators of its quality.^[75] It has recognized "evidence obtained from at least one properly randomized controlled trial" with good internal validity (i.e., a rating of "I-good") as the highest quality evidence available to it.^[75]
• The GRADE Working Group concluded in 2008 that "randomised trials without important limitations constitute high quality evidence."^[76]
• For issues involving "Therapy/Prevention, Aetiology/Harm", the Oxford Centre for Evidence-based Medicine as of 2011 defined "Level 1a" evidence as a systematic review of RCTs that are consistent with each other, and "Level 1b" evidence as an "individual RCT (with narrow Confidence Interval)."^[77]

Notable RCTs with unexpected results that contributed to changes in clinical practice include:

• After Food and Drug Administration approval, the antiarrhythmic agents flecainide and encainide came to market in 1986 and 1987 respectively.^[78] The non-randomized studies concerning the drugs were characterized as "glowing",^[79] and their sales increased to a combined total of approximately 165,000 prescriptions per month in early 1989.^[78] In that year, however, a preliminary report of an RCT concluded that the two drugs increased mortality.^[80] Sales of the drugs then decreased.^[78]
• Prior to 2002, based on observational studies, it was routine for physicians to prescribe hormone replacement therapy for post-menopausal women to prevent myocardial infarction.^[79] In 2002 and 2004, however, published RCTs from the Women's Health Initiative claimed that women taking hormone replacement therapy with estrogen plus progestin had a higher rate of myocardial infarctions than women on a placebo, and that estrogen-only hormone replacement therapy caused no reduction in the incidence of coronary heart disease.^[58][81] Possible explanations for the discrepancy between the observational studies and the RCTs involved differences in methodology, in the hormone regimens used, and in the populations studied.^[82][83] The use of hormone replacement therapy decreased after publication of the RCTs.^[84]

Disadvantages

Many papers discuss the disadvantages of RCTs.^[68][85][86] A study published in 2018, assessing the 10 most cited randomised controlled trials worldwide (each with 6,000+ citations), argues that any RCT faces a broad set of assumptions, biases and constraints – ranging from trials only being feasible to study a small set of questions amendable to randomisation and generally only being able to assess the average treatment effect of a sample, to limitations in extrapolating results to another context, among many others outlined in the study.^[87] Among the most frequently cited drawbacks are:

Time and costs

RCTs can be expensive;^[86] one study found 28 Phase III RCTs funded by the National Institute of Neurological Disorders and Stroke prior to 2000 with a total cost of US$335 million,^[88] for a mean cost of US$12 million per RCT. Nevertheless, the return on investment of RCTs may be high, in that the same study projected that the 28 RCTs produced a "net benefit to society at 10-years" of 46 times the cost of the trials program, based on evaluating a quality-adjusted life year as equal to the prevailing mean per capita gross domestic product.^[88]

The conduct of an RCT takes several years until being published, thus data is restricted from the medical community for long years and may be of less relevance at time of publication.^[89]

It is costly to maintain RCTs for the years or decades that would be ideal for evaluating some interventions.^[68][86]

Interventions to prevent events that occur only infrequently (e.g., sudden infant death syndrome) and uncommon adverse outcomes (e.g., a rare side effect of a drug) would require RCTs with extremely large sample sizes and may therefore best be assessed by observational studies.^[68]

Due to the costs of running RCTs, these usually only inspect one variable or very few variables, rarely reflecting the full picture of a complicated medical situation; whereas the case report, for example, can detail many aspects of the patient's medical situation (e.g. patient history, physical examination, diagnosis, psychosocial aspects, follow up).^[89]

Conflict of interest dangers

A 2011 study done to disclose possible conflicts of interests in underlying research studies used for medical meta-analyses reviewed 29 meta-analyses and found that conflicts of interests in the studies underlying the meta-analyses were rarely disclosed. The 29 meta-analyses included 11 from general medicine journals; 15 from specialty medicine journals, and 3 from the Cochrane Database of Systematic Reviews. The 29 meta-analyses reviewed an aggregate of 509 randomized controlled trials (RCTs). Of these, 318 RCTs reported funding sources with 219 (69%) industry funded. 132 of the 509 RCTs reported author conflict of interest disclosures, with 91 studies (69%) disclosing industry financial ties with one or more authors. The information was, however, seldom reflected in the meta-analyses. Only two (7%) reported RCT funding sources and none reported RCT author-industry ties. The authors concluded "without acknowledgment of COI due to industry funding or author industry financial ties from RCTs included in meta-analyses, readers' understanding and appraisal of the evidence from the meta-analysis may be compromised."^[90]

Some RCTs are fully or partly funded by the health care industry (e.g., the pharmaceutical industry) as opposed to government, nonprofit, or other sources. A systematic review published in 2003 found four 1986–2002 articles comparing industry-sponsored and nonindustry-sponsored RCTs, and in all the articles there was a correlation of industry sponsorship and positive study outcome.^[91] A 2004 study of 1999–2001 RCTs published in leading medical and surgical journals determined that industry-funded RCTs "are more likely to be associated with statistically significant pro-industry findings."^[92] These results have been mirrored in trials in surgery, where although industry funding did not affect the rate of trial discontinuation it was however associated with a lower odds of publication for completed trials.^[93] One possible reason for the pro-industry results in industry-funded published RCTs is publication bias.^[92] Other authors have cited the differing goals of academic and industry sponsored research as contributing to the difference. Commercial sponsors may be more focused on performing trials of drugs that have already shown promise in early stage trials, and on replicating previous positive results to fulfill regulatory requirements for drug approval.^[94]

Ethics

If a 'disruptive' innovation in medical (or other) technology is developed, it may be difficult to test this ethically in an RCT if it becomes 'obvious' that the control subjects have poorer outcomes—either due to other foregoing testing, or within the initial phase of the RCT itself. Ethically it may be necessary to abort the RCT prematurely, and getting ethics approval (and patient agreement) to withhold the innovation from the control group in future RCT's may not be feasible.

Historical control trials (HCT) exploit the data of previous RCTs to reduce the sample size; however, these approaches are controversial in the scientific community and must be handled with care.^[95]

In social science

Due to the recent emergence of RCTs in social science, the use of RCTs in social sciences is a contested issue. Some writers from a medical or health background have argued that existing research in a range of social science disciplines lacks rigour, and should be improved by greater use of randomized control trials.

Transport science

Researchers in transport science argue that public spending on programmes such as school travel plans could not be justified unless their efficacy is demonstrated by randomized controlled trials.^[96] Graham-Rowe and colleagues^[97] reviewed 77 evaluations of transport interventions found in the literature, categorising them into 5 "quality levels". They concluded that most of the studies were of low quality and advocated the use of randomized controlled trials wherever possible in future transport research.

Dr. Steve Melia^[98] took issue with these conclusions, arguing that claims about the advantages of RCTs, in establishing causality and avoiding bias, have been exaggerated. He proposed the following 8 criteria for the use of RCTs in contexts where interventions must change human behaviour to be effective:

The intervention:

1. Has not been applied to all members of a unique group of people (e.g. the population of a whole country, all employees of a unique organisation etc.)
2. Is applied in a context or setting similar to that which applies to the control group
3. Can be isolated from other activities – and the purpose of the study is to assess this isolated effect
4. Has a short timescale between its implementation and maturity of its effects

And the causal mechanisms:

5. Are either known to the researchers, or else all possible alternatives can be tested
6. Do not involve significant feedback mechanisms between the intervention group and external environments
7. Have a stable and predictable relationship to exogenous factors
8. Would act in the same way if the control group and intervention group were reversed

International development

RCTs are currently being used by a number of international development experts to measure the impact of development interventions worldwide. Development economists at research organizations including Abdul Latif Jameel Poverty Action Lab (J-PAL)^{[99][100][101]} and Innovations for Poverty Action^[102] have used RCTs to measure the effectiveness of poverty, health, and education programs in the developing world. While RCTs can be useful in policy evaluation, it is necessary to exercise care in interpreting the results in social science settings. For example, interventions can inadvertently induce socioeconomic and behavioral changes that can confound the relationships (Bhargava, 2008).

For some development economists, the main benefit to using RCTs compared to other research methods is that randomization guards against selection bias, a problem present in many current studies of development policy. In one notable example of a cluster RCT in the field of development economics, Olken (2007) randomized 608 villages in Indonesia in which roads were about to be built into six groups (no audit vs. audit, and no invitations to accountability meetings vs. invitations to accountability meetings vs. invitations to accountability meetings along with anonymous comment forms).^[103] After estimating "missing expenditures" (a measure of corruption), Olken concluded that government audits were more effective than "increasing grassroots participation in monitoring" in reducing corruption.^[103] Overall, it is important in social sciences to account for the intended as well as the unintended consequences of interventions for policy evaluations.

Criminology

A 2005 review found 83 randomized experiments in criminology published in 1982-2004, compared with only 35 published in 1957-1981.^[104] The authors classified the studies they found into five categories: "policing", "prevention", "corrections", "court", and "community".^[104] Focusing only on offending behavior programs, Hollin (2008) argued that RCTs may be difficult to implement (e.g., if an RCT required "passing sentences that would randomly assign offenders to programmes") and therefore that experiments with quasi-experimental design are still necessary.^[105]

Education

RCTs have been used in evaluating a number of educational interventions. For example, a 2009 study randomized 260 elementary school teachers' classrooms to receive or not receive a program of behavioral screening, classroom intervention, and parent training, and then measured the behavioral and academic performance of their students.^[106] Another 2009 study randomized classrooms for 678 first-grade children to receive a classroom-centered intervention, a parent-centered intervention, or no intervention, and then followed their academic outcomes through age 19.^[107]

Mock randomised controlled trials, or simulations using confectionery, can conducted in the classroom to teach students and health professionals the principles of RCT design and critical appraisal.^[108]